Mass-Spectral Method for Selection, and De-Selection, of Cancer Patients for Treatment with Immune Response Generating Therapies

ABSTRACT

A method and system for predicting in advance of treatment whether a cancer patient is likely, or not likely, to obtain benefit from administration of a yeast-based immune response generating therapy, which may be yeast-based immunotherapy for mutated Ras-based cancer, alone or in combination with another anti-cancer therapy. The method uses mass spectrometry of a blood-derived patient sample and a computer configured as a classifier using a training set of class-labeled spectra from other cancer patients that either benefitted or did not benefit from an immune response generating therapy alone or in combination with another anti-cancer therapy. Also disclosed are methods of treatment of a cancer patient, comprising administering a yeast-based immune response generating therapy, which may be yeast-based immunotherapy for mutated Ras-based cancer, to a patient selected by a test in accordance with predictive mass spectral methods disclosed herein, in which the class label for the spectra indicates the patient is likely to benefit from the yeast-based immunotherapy.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of priority under 35 U.S.C. §119(e)to U.S. Provisional Patent Application No. 61/664,308, filed Jun. 26,2012, and to U.S. Provisional Patent Application No. 61/664,329, filedJun. 26, 2012. The entire disclosure of each of U.S. Provisional PatentApplication No. 61/664,308 and U.S. Provisional Patent Application No.61/664,329 is incorporated herein by reference.

STATEMENT REGARDING JOINT RESEARCH AGREEMENT

This invention was made by or on behalf of parties to a joint researchagreement, executed Feb. 22, 2012. The parties to the joint researchagreement are: Biodesix, Inc. and GlobeImmune, Inc.

REFERENCE TO A SEQUENCE LISTING

This application contains a Sequence Listing submitted electronically asa text file by EFS-Web. The text file, named “12-621-PRO_ST25”, has asize in bytes of 19 KB, and was recorded on Mar. 15, 2013. Theinformation contained in the text file is incorporated herein byreference in its entirety pursuant to 37 CFR §1.52(e)(5).

FIELD OF THE INVENTION

This invention relates generally to the field of methods for guiding thetreatment of cancer patients. More particularly, in one aspect, thisinvention relates to a method of predicting, in advance of initiatingtreatment, whether a patient is a member of a class of patients that arelikely to benefit from administration of immune response generatingtherapies (e.g., as cellular immunotherapy agents), either alone or inaddition to standard anticancer drugs and/or other therapeutic regimentsfor the treatment of cancer. Methods of identifying patients which arenot likely to benefit from immune response generating therapies, and/orthe addition of immune response generating therapies to standardchemotherapy agents, are also disclosed. The methods of this disclosureuse mass spectral data obtained from a blood-derived sample of thepatient, a computer configured as a classifier operating on the massspectral data, and a training set comprising class-labeled spectra fromother cancer patients.

BACKGROUND OF THE INVENTION

Cancer is a broad group of various diseases, all involving unregulatedcell growth. In cancer, cells divide and grow uncontrollably, formingmalignant tumors, and invade nearby parts of the body. The cancer mayalso spread to more distant parts of the body through the lymphaticsystem or bloodstream. According to the National Cancer Institute (NCl),which tracks such statistics, the number of estimated new cases ofcancer in the United States in 2012 is 1,638,910 (not includingnon-melanoma skin cancers), and the number of deaths per year fromcancer in the United States is estimated to be 577,190(http://cancer.gov/cancertopics/cancerlibrary/what-is-cancer).Management and treatment options for cancer exist. The primary onesinclude surgery (e.g., surgical resection of a tumor), chemotherapy,radiation therapy, targeted cancer therapies (e.g., small molecule drugsor monoclonal antibody therapies that specifically target moleculesinvolved in tumor growth and progression), and palliative care, or somecombination thereof (collectively referred to herein as “anti-cancertherapies”).

Additional therapies for cancer include therapeutic strategies forinducing, enhancing or suppressing an immune response, collectivelycalled “immune therapies” or “immunotherapies” (which may also begenerally referred to herein as “immune response generating therapies”).Recently, immune therapies have become more relevant in the treatment ofadvanced or metastatic solid tumors. Immunotherapy for use in cancer isgenerally designed to augment or stimulate the patient's own immuneresponse to better control or eliminate cancerous cells, and mayadditionally support other treatments such as chemotherapy, surgery,radiation therapy and the use of targeted cancer therapies. Someexamples of such immunotherapies for use in oncology include: (1)PROVENGE® (Dendreon), in which dendritic cells are stimulated toactivate a cytotoxic response towards an antigen for use in advancedcastrate resistant prostate cancer; (2) adoptive transfer of T cells toactivate cytotoxic response to cancer; (3) genetically engineering Tcells by introducing a virus that introduces a T cell receptor that isdesigned to recognize tumor antigens; (4) Algenpantucel-L, a cancervaccine comprised of irradiated allogeneic pancreatic cancer cellstransfected to express murine alpha-1,3-galactosyltransferase withpotential antitumor activity; (5) viral vector-based immunotherapy; and(6) yeast-based immunotherapy.

Yeast-based immunotherapy is also referred to as TARMOGEN® (GlobeImmune,Inc., Louisville, Colo.) technology, and generally refers to a yeastvehicle expressing one or more heterologous target antigensextracellularly (on its surface), intracellularly (internally orcytosolically) or both extracellularly and intracellularly. Yeast-basedimmunotherapy technology has been generally described (see, e.g., U.S.Pat. No. 5,830,463). Certain yeast-based immunotherapy compositions, andmethods of making and generally using the same, are also described indetail, for example, in U.S. Pat. No. 5,830,463, U.S. Pat. No.7,083,787, U.S. Pat. No. 7,736,642, Stubbs et al., Nat. Med. 7:625-629(2001), Lu et al., Cancer Research 64:5084-5088 (2004), and in Bernsteinet al., Vaccine 2008 Jan. 24; 26(4):509-21, each of which isincorporated herein by reference in its entirety. Yeast-basedimmunotherapy for cancer is described, for example, in U.S. Pat. No.7,465,454, U.S. Pat. No. 7,563,447, U.S. Pat. No. 8,067,559, U.S. Pat.No. 8,153,136, U.S. Patent Publication No. 2009-0098154, and PCTPublication No. WO 07/133,835, each of which is incorporated herein byreference in its entirety.

Yeast-based immunotherapy has a unique ability, as compared to otherimmunotherapies, to induce innate immune responses as well as a widerange of adaptive immune responses against the target antigen, includingCD4-dependent TH17 and TH1 T cell responses and antigen-specific CD8⁺ Tcell responses, which include cytotoxic T lymphocyte (CTL) responses,all without the use of exogenous adjuvants, cytokines, or otherimmunostimulatory molecules, many of which have toxicity issues. Inaddition, yeast-based immunotherapy compositions inhibit regulatory Tcell (Treg) numbers and/or functionality, thereby enhancing effector Tcell responses that might normally be suppressed by the presence of thetumor, for example. Moreover, as compared to immunotherapeuticcompositions that immunize by generating antibody responses, theantigen-specific, broad-based, and potent cellular immune responseselicited by yeast-based immunotherapy are believed to be particularlyeffective in targeting tumor cells, even in the face of what mayotherwise be a suppressive environment. Since this type of immunotherapyutilizes the natural ability of the antigen presenting cell to presentrelevant immunogens, it is not necessary to know the precise identity ofCTL epitopes or Class II MHC epitopes of a target antigen to produce aneffective yeast-based immunotherapeutic, nor is it necessary to isolateany immune cells from the patient to produce the immunotherapeutic. Infact, multiple CD4⁺ and CD8⁺ T cell epitopes can be targeted in a singleyeast-based immunotherapeutic composition, and so the use of algorithmsand complex formulas to identify putative T cell epitopes or T cellreceptors is eliminated.

One series of yeast-based immunotherapy products, including theTARMOGEN® product candidates known as “GI-4000” currently in clinicaldevelopment by GlobeImmune, Inc., has been developed to stimulate immuneresponses against a mutated Ras protein expressed by a patient's tumor.“Ras” is the name given to a family of related proteins found insidecells, including human cells. All Ras protein family members belong to aclass of protein called small GTPase, and are involved in transmittingsignals within cells (cellular signal transduction). Ras mutations arefound in approximately 180,000 new cancer cases each year in the UnitedStates across a spectrum of tumor types, including pancreas, non-smallcell lung cancer (NSCLC), colorectal, endometrial and ovarian cancers,as well as melanoma and multiple myeloma. Studies have shown that tumorswith Ras mutations are generally less responsive than tumors with normalRas to conventional chemotherapy as well as targeted agents. For somecancers, such as NSCLC or colorectal cancer, therapies that targetepidermal growth factor receptor, or EGFR, have improved clinicaloutcomes. However, the presence of a Ras mutation in the tumor has beenassociated with poor prognosis despite use of EGFR targeted therapies incolorectal cancer. Similarly, other studies have shown that patientswith Ras-mutated colorectal tumors do not benefit from cetuximabtherapy, another EGFR targeted agent, compared to patients with normalRas, who have improved survival rates when treated with the sametherapy. As a result, patients with Ras mutations have fewer availableeffective treatment options. The targeted reduction of cells containingRas mutations could result in improved clinical outcomes for patientswith a number of human cancers due to the role mutated Ras plays intumor growth. However, there are presently no available therapiestargeting mutated Ras in late-stage clinical trials.

Progress in the field of immunotherapy has been slow, but recentclinical successes have given strong support to the potential of thisapproach as a treatment modality in cancer. However, there is a need inthe art to define biomarkers which identify patients who will obtainclinical benefit from immune-based treatment in cancer and identifyclinical responders and non-responders, in advance of treatment.Examples of immunotherapy markers include CD54 expression andinterleukin 12p70 production, but they have not been fully validated.Also used are several cellular immune marker assays (cytokine flowcytometry, MHC tetramers, and enzyme-linked immunosorbent spot(ELISPOT)). It is important to note that assays predicting benefit fromimmunotherapies need to be standardized to produce reproducible andcomparable results. This has not been done in this area.

There is a need in the art for practical, useful tests for determining,in advance of treatment, whether a given cancer patient is likely tobenefit from administration of immune response generating therapies,either alone or in combination with other anti-cancer drug therapies,or, conversely whether such treatment is not likely to benefit a givencancer patient. This invention meets this need.

Further prior art of interest relating to ability to predict cancerpatient benefit from certain types of drugs includes U.S. Pat. Nos.7,736,905, 7,858,390; 7,858,389, 7,867,775, 8,024,282; 7,906,342 and7,879,620, and pending U.S. patent application Ser. No. 13/356,730 filedJan. 24, 2012, and U.S. patent application Ser. No. 12/932,295 filedFeb. 22, 2011, published as US 2011/0208433, all of which are assignedto Biodesix, Inc. The '905 patent and U.S. patent application Ser. No.12/932,295 filed Feb. 22, 2011 are incorporated by reference herein. The'905 patent describes, among other things, a mass spectrometry basedtest for determining whether NSCLC cancer patients are likely to benefitfrom epidermal growth factor receptor (EGFR) targeting drugs. This testis known in its commercial version as “VeriStrat”; references to“VeriStrat” in the following discussion will be understood to be inreference to the test described in the '905 patent.

SUMMARY OF THE INVENTION

This invention relates generally to the field of methods for guiding thetreatment of cancer patients. In one aspect, such treatment of cancerpatients is immunotherapy for cancer, and in one aspect, the treatmentis yeast-based immunotherapy for cancer, and in yet another aspect, thetreatment is yeast-based immunotherapy for mutated Ras-positive cancers(i.e., cancers where at least some tumors are positive for a mutated Rasprotein, typically detected by detecting mutations in the ras nucleotidesequence). In one aspect, the treatment is yeast-based immunotherapy formutated Ras-positive pancreas cancer.

More particularly, in one aspect, this invention relates to a method ofpredicting, in advance of initiating treatment, whether a cancer patientis a member of a class of patients that are likely to benefit fromadministration of yeast-based immune response generating therapies(e.g., as cellular immunotherapy agents), either alone or in addition totreatment with standard anti-cancer drugs and/or other therapeuticregiments for the treatment of cancer. Methods of identifying patientswhich are not likely to respond to yeast-based immunotherapies, and/orthe addition of immunotherapies to standard chemotherapy agents are alsodisclosed. Methods of identifying patients which are less likely, or notlikely, to respond to yeast-based immunotherapy, and/or the addition ofyeast-based immunotherapy to standard chemotherapy agents and/or othertreatments for cancer (e.g., surgical resection) are also disclosed.

In yet another aspect, this invention relates to a method of predicting,in advance of initiating treatment, whether a patient is a member of aclass of patients that are likely to benefit from administration ofyeast-based immunotherapy for mutated Ras-positive cancer, either aloneor in addition to standard anti-cancer drugs and/or other therapeuticregiments for the treatment of cancer. Methods of identifying patientswhich are less likely, or not likely, to respond to yeast-basedimmunotherapy for mutated Ras-positive cancer, and/or the addition ofyeast-based immunotherapy for mutated Ras-positive cancer to standardchemotherapy agents and/or other treatments for cancer (e.g., surgicalresection) are also disclosed. In one aspect, the mutated Ras-positivecancer is pancreas cancer. As one example, this document describes amethod for predicting whether pancreas cancer patients are likely tobenefit from administration of yeast-based immunotherapy targetingmutated Ras (e.g., the series of products known as GI-4000, described inmore detail herein) in combination with administration of gemcitabine.

The methods of this disclosure use mass spectral data obtained from ablood-derived sample of the patient, a computer configured as aclassifier operating on the mass spectral data, and a training setcomprising class-labeled spectra from other cancer patients.

The applicants have discovered a method of predicting, in advance oftreatment, whether a cancer patient is likely or not likely to benefitfrom administration of a yeast-based immune response generating therapy,either alone or in combination with another anti-cancer therapy. Themethod is based on mass spectrometry of a blood-derived sample. The useof blood-derived samples (e.g., serum, plasma) is significant, as itincreases the likelihood of measuring global susceptibility toimmunotherapies by giving insight into circulating markers of the immunesystem. Furthermore, the methods can be conducted quickly via a simplemass spectrometry test from a blood-derived sample, without the need forperforming complex, time consuming assays of a patient sample orobtaining a tumor sample from the patient. Notably, the applicants havedemonstrated the validity of its test from samples obtainedpre-treatment. Accordingly, practical implementations of the test usepre-treatment samples from the patient and predict whether the patientis likely, or not likely, to benefit from a yeast-based immune responsegenerating therapy.

The methods of this disclosure take the form of practical, useful testswhich can be performed with the aid of a mass spectrometer (e.g., MALDITOF instrument) and a general purpose computer configured to function asa classifier.

In one aspect, a method of predicting whether a cancer patient is likelyto benefit from administration of a yeast-based immune responsegenerating therapy, either alone or in addition to other anti-cancertherapies, is described comprising the steps of:

(a) obtaining a blood-derived sample of the patient;

(b) conducting mass-spectrometry on the sample and obtaining a massspectrum from the sample;

(c) in a programmed computer, performing one or more predefinedpre-processing steps on the mass spectrum, obtaining integratedintensity values of selected features in the mass spectrum overpredefined m/z ranges after the pre-processing steps are performed, andcomparing the integrated intensity values with a training set comprisingclass-labeled spectra from other cancer patients and thereby classifyingthe mass spectrum with a class label. The class label predicts whetherthe patient is likely, or not likely, to benefit from the immuneresponse generating therapy either alone or in addition to otheranti-cancer therapies. For example, the class label may take the form of“Slow” or “Quick”, with “Slow” indicating that the patient is likely tobenefit and the time to recurrence or disease progress of the cancer isrelatively slow, whereas “Quick” may indicate that the patient is notlikely to benefit and the time to recurrence or disease progressing isrelatively brief. Of course, other equivalent class labels could beused, such as “benefit”, “non-benefit”, “good”, “poor” or the like.

In one aspect, a method of predicting whether a cancer patient is likelyto benefit from administration of yeast-based immunotherapy for mutatedRas-positive cancer, either alone or in addition to other anti-cancertherapies, is described comprising the steps of:

(a) obtaining a blood-derived sample of the patient to be treated withyeast-based immunotherapy for mutated Ras-positive cancer, alone or incombination with other anti-cancer therapies;

(b) conducting mass-spectrometry on the sample and obtaining a massspectrum from the sample;

(c) in a programmed computer, performing one or more predefinedpre-processing steps on the mass spectrum, obtaining integratedintensity values of selected features in the mass spectrum overpredefined m/z ranges after the pre-processing steps are performed, andcomparing the integrated intensity values with a training set comprisingclass-labeled spectra from other cancer patients prior to theirtreatment with yeast-based immunotherapy for mutated Ras-positive cancerand thereby classifying the mass spectrum with a class label. The classlabel predicts whether the patient is likely, or not likely, to benefitfrom the yeast-based immunotherapy for mutated Ras-positive cancereither alone or in addition to other anti-cancer therapies. For example,the class label may take the form of “Slow” or “Quick”, with “Slow”indicating that the patient is likely to benefit and the time torecurrence or disease progress of the cancer is relatively slow, whereas“Quick” may indicate that the patient is not likely to benefit and thetime to recurrence or disease progressing is relatively brief. As above,other equivalent class labels could be used, such as “benefit”,“non-benefit”, “good”, “poor” or the like. In one specific embodiment ofthe invention, the cancer patient for which the test is performed is apancreas cancer patient. In this embodiment, the yeast-basedimmunotherapy for mutated Ras-positive cancer may take the form ofGI-4000 (described in detail below) or the equivalent. In one aspect,the yeast-based immunotherapy for mutated Ras-positive cancer isadministered to the patient in conjunction with gemcitabine or theequivalent. In one aspect of this embodiment of the invention, themutated Ras-positive cancer can include, but is not limited to, pancreascancer, non-small cell lung cancer (NSCLC), colorectal cancer (CRC),endometrial cancers, ovarian cancers, melanoma and multiple myeloma.

In one aspect, any of the above-described methods of predictingdescribed above or elsewhere herein are considered applicable to othercancer patients, including for example, but not limited to, non-smallcell lung cancer (NSCLC) patients and colorectal cancer (CRC) patients,either alone or as an adjuvant to other standard anti-cancer agents, asthe mass spectral features which are useful for classification in thisdisclosure are believed to be associated with, among other things,regulation of cellular inflammation response, and predictive across abroad range of tumor types as explained in U.S. patent application Ser.No. 12/932,295 filed Feb. 22, 2011 and the previously cited patents ofBiodesix, Inc.

The training set used in any of the methods described above or elsewhereherein is preferably in the form of class-labeled spectra from othercancer patients who obtained benefit and who did not obtain benefit fromadministration of the immune response generating therapy either alone orin combination with another anti-cancer therapy. The features (m/zranges) in the patient's spectrum that are used in classification can beinvestigated and selected from analysis of the mass spectra of thepatients forming the training set. We speculated that one or more of thefeatures used in U.S. Pat. No. 7,736,905 and listed in Tables 3 and 4herein, which were developed in an entirely different context ofpredicting NSCLC patient benefit from Epidermal Growth Factor ReceptorInhibitors (EGFR-Is) are a suitable set of features for use in theinstant methods because they could relate to the hosts immunological andinflammatory response to a tumor (see US patent application publication2011/0208433). (The precise feature values used for classification mayvary from the list set forth in '905 patent and in Tables 1-4 below,e.g., depending on the spectra alignment (shift) that is performedduring pre-processing of the spectra because the sample type issubstantially different.) Unlike the training set used in the '905patent (spectra from NSCLC patients that either did or did not respondto EGFR targeting drugs), the training set used in the present methodsuses spectra from samples of patients who obtained benefit and who didnot obtain benefit from administration of the immune response generatingtherapy either alone or in combination with another anti-cancer therapy.Note, that the sample type in the current application is different fromserum and plasma which was described in the '905 patent. However, asexplained below, there are other features in the spectra that could beused for classification from an investigation of the spectra forming thetraining set examples of which are given herein.

In another aspect of this disclosure, a method of treating a cancerpatient is described comprising the steps of: conducting a test inaccordance with any of the methods of predicting described above orelsewhere herein, and if the class label for the spectra indicates thepatient is likely to benefit from the yeast-based immune responsegenerating therapy, administering a yeast-based immune responsegenerating therapy either alone or in combination with anotheranti-cancer agent to the patient.

In one aspect, the patient is additionally treated with one or moreadditional anti-cancer therapies, either prior to, concurrently with, orafter, treatment with the yeast-based immunotherapy for cancer. In oneembodiment, the additional anti-cancer therapies include, but are notlimited to, surgery (e.g., surgical resection of a tumor), chemotherapy,radiation therapy, targeted cancer therapies (e.g., small molecule drugsor monoclonal antibody therapies that specifically target moleculesinvolved in tumor growth and progression), and palliative care, or anycombination thereof.

In another aspect of this disclosure, a method of treating a cancerpatient with yeast-based immunotherapy for cancer is described,comprising the step of: administering yeast-based immunotherapy forcancer to a cancer patient selected by a test in accordance with any ofthe methods of predicting described above or elsewhere herein in whichthe class label for the spectra indicates the patient is likely tobenefit from the yeast-based immunotherapy for cancer. In one aspect,the patient is additionally treated with one or more additionalanti-cancer therapies, either prior to, concurrently with, or after,treatment with the yeast-based immunotherapy for cancer. In oneembodiment, the additional anti-cancer therapies include, but are notlimited to, surgery (e.g., surgical resection of a tumor), chemotherapy,radiation therapy, targeted cancer therapies (e.g., small molecule drugsor monoclonal antibody therapies that specifically target moleculesinvolved in tumor growth and progression), and palliative care, or anycombination thereof.

In yet another aspect of this disclosure, a method of treating a cancerpatient with yeast-based immunotherapy for mutated Ras-positive canceris described, comprising the steps of: conducting a test in accordancewith any of the methods of predicting described above or elsewhereherein, and if the class label for the spectra indicates the patient islikely to benefit from yeast-based immunotherapy for mutatedRas-positive cancer, administering the yeast-based immunotherapy formutated Ras-positive cancer. In this aspect of the invention, thepatient has a cancer in which mutated Ras has been identified in atleast some of the tumor cells from the patient. In one aspect, thepatient is additionally treated with one or more additional anti-cancertherapies, either prior to, concurrently with, or after, treatment withthe yeast-based immunotherapy for mutated Ras-positive cancer. In oneembodiment, the yeast-based immunotherapy for mutated Ras-positivecancer is a product in the series of yeast-based immunotherapy productsknown as GI-4000, or the equivalent. In one aspect of this embodiment ofthe invention, the mutated Ras-positive cancer can include, but is notlimited to, pancreas cancer, non-small cell lung cancer (NSCLC),colorectal cancer (CRC), endometrial cancers, ovarian cancers, melanomaand multiple myeloma. In one aspect, the cancer is pancreas cancer. Inone embodiment, the additional anti-cancer therapies include, but arenot limited to, surgery (e.g., surgical resection of a tumor),chemotherapy, radiation therapy, targeted cancer therapies (e.g., smallmolecule drugs or monoclonal antibody therapies that specifically targetmolecules involved in tumor growth and progression), and palliativecare, or any combination thereof. In one aspect, the yeast-basedimmunotherapy for mutated Ras-positive cancer is administered to thepatient in conjunction with gemcitabine or the equivalent. In oneembodiment, the patient is a pancreas cancer patient and the therapycomprises a product in the series of yeast-based immunotherapy productsknown as GI-4000 or the equivalent (described in detail below), eitheralone or in combination with gemcitabine or the equivalent. In oneaspect, the cancer patient's tumor has been surgically resected prior totreatment with the yeast-based immunotherapy composition.

In another aspect of this disclosure, a method of treating a cancerpatient with yeast-based immunotherapy for cancer is described,comprising the step of: administering yeast-based immunotherapy formutated Ras-positive cancer to a cancer patient selected by a test inaccordance with any of the methods of predicting described above orelsewhere herein in which the class label for the spectra indicates thepatient is likely to benefit from the yeast-based immunotherapy formutated Ras-positive cancer. In this aspect of the invention, thepatient has a cancer in which mutated Ras has been identified in tumorcells from the patient. In one aspect, the patient is additionallytreated with one or more additional anti-cancer therapies, either priorto, concurrently with, or after, treatment with the yeast-basedimmunotherapy for mutated Ras-positive cancer. In one embodiment, theyeast-based immunotherapy for mutated Ras-positive cancer is a productin the series of yeast-based immunotherapy products known as GI-4000, orthe equivalent. In one aspect of this embodiment of the invention, themutated Ras-positive cancer can include, but is not limited to, pancreascancer, non-small cell lung cancer (NSCLC), colorectal cancer (CRC),endometrial cancers, ovarian cancers, melanoma and multiple myeloma. Inone aspect, the cancer is pancreas cancer. In one embodiment, theadditional anti-cancer therapies include, but are not limited to,surgery (e.g., surgical resection of a tumor), chemotherapy, radiationtherapy, targeted cancer therapies (e.g., small molecule drugs ormonoclonal antibody therapies that specifically target moleculesinvolved in tumor growth and progression), and palliative care, or anycombination thereof. In one aspect, the yeast-based immunotherapy formutated Ras-positive cancer is administered to the patient inconjunction with gemcitabine or the equivalent. In one embodiment, thepatient is a pancreas cancer patient and the therapy comprises a productin the series of yeast-based immunotherapy products known as GI-4000 orthe equivalent, either alone or in combination with gemcitabine or theequivalent. In one aspect, the cancer patient's tumor has beensurgically resected prior to treatment with the yeast-basedimmunotherapy composition.

In any of the aspects of a method to treat a patient with cancer using ayeast-based immunotherapy, the yeast-based immunotherapy can include,but is not limited to, a whole, heat-inactivated recombinant yeast thathas expressed at least one cancer antigen associated with or expressedby the patient's tumor. In one aspect, the yeast can be from a genus ofyeast including, but not limited to, Saccharomyces. In one aspect, theyeast can be from a species of yeast including, but not limited to,Saccharomyces cerevisiae.

In another aspect, a system is disclosed for predicting whether a cancerpatient is likely to benefit from administration of a yeast-based immuneresponse generating therapy either alone or in combination with anotheranti-cancer agent. The system includes a mass spectrometer generating amass spectrum from a blood-derived sample from the cancer patient. Thesystem also includes a machine-readable memory storing a training set ofclass-labeled spectra from other cancer patients. The training setincludes class-labeled spectra from plurality of patients that did notbenefit from yeast-based immune response generating therapy either aloneor in combination with another anti-cancer agent and class-labeledspectra from plurality of patients that did benefit from the cellularimmunotherapy either alone or in combination with the anotheranti-cancer agent. The system further includes a computer systemconfigured to operate on the mass spectrum and classify the massspectrum using the training set, producing a class label for the massspectrum, wherein the class label is used to predict whether the patientis likely to benefit from administration of the yeast-based immuneresponse generating therapy either alone or in combination with anotheranti-cancer agent.

In another aspect, a system is disclosed for predicting whether a cancerpatient is likely to benefit from administration of yeast-basedimmunotherapy for cancer, either alone or in conjunction with treatmentwith another anti-cancer therapy. The system includes a massspectrometer generating a mass spectrum from a blood-derived sample fromthe cancer patient. The system also includes a machine-readable memorystoring a training set of class-labeled spectra from other cancerpatients. The training set includes class-labeled spectra from pluralityof patients that did not benefit from the yeast-based immunotherapy forcancer either alone or in conjunction with treatment with anotheranti-cancer therapy and class-labeled spectra from plurality of patientsthat did benefit from the yeast-based immunotherapy for cancer eitheralone or in conjunction with treatment with the another anti-cancertherapy. The system further includes a computer system configured tooperate on the mass spectrum and classify the mass spectrum using thetraining set, producing a class label for the mass spectrum, wherein theclass label is used to predict whether the patient is likely to benefitfrom administration of the yeast-based immunotherapy for cancer eitheralone or in conjunction with treatment with another anti-cancer therapy.

In another aspect, a system is disclosed for predicting whether a cancerpatient is likely to benefit from administration of yeast-basedimmunotherapy for mutated Ras-positive cancer, either alone or inconjunction with treatment with another anti-cancer therapy. The systemincludes a mass spectrometer generating a mass spectrum from ablood-derived sample from the cancer patient. The system also includes amachine-readable memory storing a training set of class-labeled spectrafrom other cancer patients. The training set includes class-labeledspectra from plurality of patients that did not benefit from theyeast-based immunotherapy for mutated Ras-positive cancer either aloneor in conjunction with treatment with another anti-cancer therapy andclass-labeled spectra from plurality of patients that did benefit fromthe yeast-based immunotherapy for mutated Ras-positive cancer eitheralone or in conjunction with treatment with the another anti-cancertherapy. The system further includes a computer system configured tooperate on the mass spectrum and classify the mass spectrum using thetraining set, producing a class label for the mass spectrum, wherein theclass label is used to predict whether the patient is likely to benefitfrom administration of the yeast-based immunotherapy for mutatedRas-positive cancer either alone or in conjunction with treatment withanother anti-cancer therapy. In one aspect, the yeast-basedimmunotherapy for mutated Ras-positive cancer is a product within theseries of products known as GI-4000. In one aspect, the mutatedRas-positive cancer is selected from pancreas cancer, non-small celllung cancer (NSCLC), colorectal cancer (CRC), endometrial cancers,ovarian cancers, melanoma and multiple myeloma. In one aspect, thecancer is pancreas cancer.

BRIEF DESCRIPTION OF THE DRAWINGS

The following detailed description will make reference to the appendeddrawings, which are offered by way of example and not limitation, and inwhich:

FIG. 1 is a schematic drawing showing the design of a Phase 2 clinicaltrial in pancreas cancer using GlobeImmune's yeast-based immunotherapyproduct series targeting mutated Ras-positive cancers, known as GI-4000.

FIGS. 2A and 2B are Kaplan-Meier plots or recurrence free survival (RFS)and overall survival (OS) illustrating ability of a mass spectrometrymethod of this disclosure to identify patients which are likely tobenefit from the combination of GI-4000 and gemcitabine in the treatmentof pancreatic cancer.

FIGS. 3A-3F are pairs of Kaplan-Meier plots of RFS and OS for patientsin both the treatment and control arms in the study of GI-4000 andgemcitabine in the treatment of pancreatic cancer for different valuesof K used in a K-nearest neighbor classification algorithm. In FIGS.3A-3B, the value of K was 1, in FIGS. 3C and 3D the value of K was 3 andin 3E and 3F the value of K was 5. The plots show the ability of theapplicant's classifier to separate, in the treatment arm (patientstreated with both gemcitabine and GI-4000), patients which benefitedfrom those that did not, and moreover that some patients in thetreatment arm did worse than some patients in the control arm.Therefore, the plots demonstrate the ability of the applicant's massspectral method to both predict those patients that are likely tobenefit from the treatment with immune response generating therapies aswell as those patients that are not likely to benefit from the treatmentwith immune response generating therapies.

FIGS. 4A-4H are sets of Kaplan-Meier plots of RFS and OS in the GI-4000and gemcitabine study as defined by a classifier based on spectraobtained using 150,000 shots with the “DeepMALDI” method of massspectrometry of blood-derived samples, using the techniques describedherein and in U.S. provisional patent application 61/652,394 filed May29, 2012, the content of which is incorporated by reference herein.FIGS. 4A-4B, 4C-4D, 4E-4F, and 4G-4H each represent plots for RFS and OSfrom four different classifiers, respectively, based on different setsof peaks in mass spectra used for classification.

FIG. 5 is a flow chart of a cross-validation process for classifiervalidation used in the pancreatic cancer/GI-4000 and gemcitabine study.

FIG. 6 is a plot of the distributions of hazard ratios between “Quick”and “Slow” groups for RFS from cross-validation analysis of the“dilute-and-shoot” classifier of FIGS. 3E-3F.

FIG. 7A is a plot of the distributions of median RFS in “Quick” and“Slow” groups in the Test Set of the cross-validation analysis in theGI-4000 and gemcitabine study. FIG. 7B is a plot of the distribution ofdifference in medians between “Quick” and “Slow” groups in the Test Setof the cross-validation analysis of the GI-4000 and gemcitabine study.Both plots use the “dilute-and-shoot” classifier of FIGS. 3E-3F.

FIG. 8 is a plot of the distribution of medians of “Quick” and “Slow”groups in the Control Arm and the Test Set in the cross-validationanalysis of the “dilute-and-shoot” classifier of FIGS. 3E-3F.

FIG. 9 is a plot of the distribution of difference in medians betweenthe Test Set and Control Arm for the “Slow” group in thecross-validation analysis of the “dilute-and-shoot” classifier of FIGS.3E-3F.

FIG. 10A is a plot of the distribution of the ratio of Slow to Quickclassifications in the control arm for different values of K used in theclassifier in the cross-validation analysis of the “dilute-and-shoot”classifier of FIGS. 3E-3F. FIG. 10B is a plot of Hazard ratios for theTest Set used in cross validation analyses for various values of K usedin the K-nearest neighbor classifier.

FIG. 11 is a flow chart of a testing method for predicting cancerpatient benefit, or non-benefit from immune response generatingtherapies either alone or in combination with other anti-cancer agents.

FIG. 12 is an illustration of a system for performing testing of ablood-derived patient sample and predicting whether the patient islikely to benefit from an immune response generating therapy eitheralone or in combination with another anti-cancer agent.

FIGS. 13A-13L are sets of Kaplan-Meier plots of RFS and OS in theGI-4000 and gemcitabine study as defined by classifiers based on spectraobtained from the DeepMALDI method using 500,000 shots, using techniquesof pending U.S. provisional patent application 61/652,394 filed May 29,2012, the content of which is incorporated by reference herein. FIGS.13A-13B, 13C-13D, 13E-13F, 13G-13H, 13I-13J, and 13K-13L each representplots for RFS and OS from six different classifiers, respectively, basedon different sets of peaks or features in mass spectra used forclassification.

FIG. 14 is a plot of the distribution of hazard ratios between “Quick”and “Slow” groups for RFS from cross-validation analysis on theclassifiers of FIGS. 4A-4H developed using spectra obtained from theDeepMALDI method using 150,000 shots.

FIG. 15 is a plot of the distribution of hazard ratios between “Quick”and “Slow” groups for RFS from cross-validation analysis on theclassifiers of FIGS. 13A-13L developed using spectra obtained from theDeepMALDI method using 500,000 shots.

FIGS. 16A-16C are an illustration of three MALDI mass spectra of thesame sample in a selected mass/charge range (m/z ratio 7,000 to 8,000),illustrating the increase in detectable peak content with increasingnumber of shots. The spectrum of FIG. 16A resulted from 2,000 shots, thespectrum of FIG. 16B resulted from 100,000 shots, and the spectrum ofFIG. 16C resulted from 500,000 shots. Note how the spectra of FIGS. 16Band 16C, resulting from our methods, reveal a wealth of spectralinformation on the sample which was not present in the spectrum of FIG.16A, which appears essentially as noise.

FIGS. 16D and 16E are further examples of mass spectra showing theenormous dynamic range of spectra obtained in our DeepMALDI method. InFIG. 16D, a portion of the spectrum in an m/z range from 7140 to 7890 Dais shown enlarged in the inset of FIG. 16D showing a wealth of spectralinformation obtained at approximately 500,000 shots. In FIG. 16E, thespectrum is shown in the inset with the Y axis amplified in order toshow additional spectral information and peaks in the region of m/zaround 9520, which are revealed with the DeepMALDI method but which arenot visible in a typical ˜1,000 shot spectrum.

FIG. 17A is a plan view of a MALDI-TOF target plate containing 384sample spots or “spots” arranged in a rectangular array. The spots areidentified by column numbers 1 . . . 24 and rows A . . . P, e.g., theupper left spot is identified as A1. FIG. 17B is an enlarged view of anindividual sample spot P1 which is shown divided into a 5×5 rectangulargrid having X/Y location coordinates and an origin (0,0) at the centerof the spot. The rectangular grid and location coordinates are used inan automated raster scanning approach to acquire spectra from 100,000 ormore shots from the spot as described in detail herein.

FIG. 18 is a photograph of a biological sample/matrix mixture depositedin a single spot in the MALDI plate of FIG. 17A. Ideally, the spotcontains a uniform, homogenous crystallized sample within the spot, asshown in FIG. 18.

FIG. 19 is an illustration of one possible raster scanning pattern foruse in obtaining 100,000 or more shots from the spot of FIG. 18. Thespot is raster scanned multiple times, e.g., 25 times. Each symbol set(triangle, square, X, etc.) shown in FIG. 19 depicts a set ofindividual, discrete X/Y locations where the spot is scanned (shot) in asingle raster scan. At each location, the spot can be subject tomultiple shots, e.g., 700 or 800 shots.

FIG. 20 is an illustration showing the superposition of the rasterscanning pattern of FIG. 19 on the sample spot of FIG. 18.

FIG. 21 is a screen shot from a MALDI-TOF instrument user interfaceshowing commands for summing accumulated spectra from 800 laser shotsper location/raster, e.g., in the raster scanning of FIG. 17B or 20.

FIG. 22 is an image of a portion of a sample spot showing areas wherethe sample/matrix mixture does not crystallize in a spatially uniformmanner.

FIG. 23 is a screen shot from a MALDI-TOF instrument user interfaceshowing an image of a portion of a spot captured by a camera in theinstrument, and the selection of a group of spots for automated rasterscanning of the spots.

FIG. 24 is another screen shot from a MALDI-TOF instrument userinterface showing tools for evaluation of spectra, accumulation ofspectra, and movement of a laser across a spot for firing in differentpatterns.

FIG. 25 is a screen shot of an evaluation page for accepting orrejecting transient spectra during data acquisition.

FIG. 26 is a screen shot showing exclusion lists for eliminatingbackground peaks.

DETAILED DESCRIPTION, WORKING EXAMPLES AND EXPERIMENTAL RESULTS

Described herein are predictive tests for immune response generatingtherapies, related classifiers and systems and treatment of patientsidentified by these tests, classifiers and systems.

In particular, methods are described herein for predicting, in advanceof treatment, whether a cancer patient is likely or not likely tobenefit from administration of a yeast-based immune response generatingtherapy, either alone or in combination with another anti-cancertherapy. Methods are also described herein for predicting, in advance oftreatment, whether a cancer patient is likely or not likely to benefitfrom administration of yeast-based immunotherapy for mutatedRas-positive cancer (e.g., GI-4000 described herein), including, but notlimited to, pancreas cancer. The methods of the invention are based onmass spectrometry of a blood-derived sample (e.g., serum or plasma)obtained pre-treatment, and classification based on the proteomicsignature in the sample revealed by mass spectrometry. The use ofblood-derived samples is significant, as it increases the likelihood ofmeasuring global susceptibility to immunotherapy by giving insight intocirculating markers of the immune system. Furthermore, the methods canbe conducted quickly via a simple mass spectrometry test from ablood-derived sample, without the need for performing complex, timeconsuming assays of a patient sample or obtaining a tumor sample fromthe patient. The tests are useful in that, if the patient is predictedto be likely to benefit the treatment can proceed with some confidencethat the patient will have an improved outcome, whereas if the patientis predicted in advance that they are not likely to benefit, the patientcan be steered towards other treatments in which the patient is likelyto derive benefit, or other treatment options can be considered.

Methods are also described herein for treating a patient with ayeast-based immune response generating therapy for cancer, either aloneor in combination with another anti-cancer agent, where the patient hasfirst been selected by a method or test in accordance with any of themethods of predicting described above or elsewhere herein to be likelyto benefit from the immune response generating therapy for cancer (e.g.,the class label for the spectra generated in the test or methodindicates the patient is likely to benefit from the immune responsegenerating therapy for cancer).

Methods are also described herein for treating a patient withyeast-based immunotherapy for mutated Ras-positive cancer (e.g., GI-4000described herein), either alone or in combination with anotheranti-cancer agent, where the patient has first been selected by a methodor test in accordance with any of the methods of predicting describedabove or elsewhere herein to be likely to benefit from the yeast-basedimmunotherapy for mutated Ras-positive cancer (e.g., the class label forthe spectra generated in the test or method indicates the patient islikely to benefit from the yeast-based immunotherapy for mutatedRas-positive cancer). In any of these methods of treating of theinvention, the method includes a step of administering the yeast-basedimmune response generating therapy (which may include, but is notlimited to, a yeast-based immunotherapy for mutated Ras-positive cancer)to a subject that has a cancer expressing the cancer antigen, and whohas been identified or selected as likely to benefit from administrationof the composition by a test performed in accordance with any of themethods of predicting of the invention as described herein.

A Working Example Describing GI-4000-02: A Phase 2b Clinical Trial forGI-4000 and Gemcitabine in Pancreas Cancer

GI-4000-02 is a fully-enrolled Phase 2b randomized, double-blind,placebo-controlled, multi-center, adjuvant clinical trial of GI-4000plus gemcitabine or placebo plus gemcitabine in patients with R0 or R1resected pancreas cancer (see FIG. 1). An R0 resection is defined by theabsence of microscopic residual disease at the surgical margin. An R1resection is defined by the presence of microscopic residual disease atthe surgical margin. R0 and R1 patients have different expected survivalrates, with R0 patients living longer on average. In this clinicaltrial, a sample of tumor tissue was obtained from each subject duringthe screening period and the tumor was evaluated for the presence of aRas mutation. If a subject had a product-related mutation, then theGI-4000 yeast-based immunotherapy product that matched the specific Rasmutation in the subject's tumor was administered (the GI-4000 series isdescribed in detail below).

The study population consisted of 176 subjects with Ras mutated resectedpancreas cancer enrolled at 39 centers in the United States and fiveinternational centers. Following resection, subjects were prospectivelystratified into two groups by resection status, and both the R1 and R0groups were randomly assigned into two treatment groups at a one-to-oneratio to receive either 40Y.U. of GI-4000 (“Y.U.” is a “Yeast Unit” or“yeast cell equivalent; one Y.U.=10 million yeast cells) plusgemcitabine or placebo plus gemcitabine. Thirty-nine R1 subjects wereenrolled, of whom 19 were assigned to the GI-4000 plus gemcitabine groupand 20 were assigned to the placebo plus gemcitabine group. One hundredthirty-seven R0 subjects were enrolled, of whom 69 were assigned to theGI-4000 plus gemcitabine group and 68 were assigned to the placebo plusgemcitabine group. The 40 Y.U. dose of GI-4000 was administered as fourseparate 10Y.U. subcutaneous injections, one in each arm and leg.Subjects were given three weekly doses of either GI-4000 or placebobetween resection and the initiation of gemcitabine therapy. Allsubjects were administered up to six monthly cycles of gemcitabinebeginning between six and eight weeks after resection. Monthly doses ofGI-4000 or placebo were given after each cycle of gemcitabine tocoincide with the scheduled breaks in monthly gemcitabine treatment.Monthly administration of GI-4000 or placebo continued until subjectswithdrew from the study, experienced disease recurrence or died. Anumber of disease-specific baseline characteristics were evaluated,including the following prognostic factors, which have been shown tohave an impact on outcome: (a) Lymph node status was defined by thepresence or absence of microscopic evidence of pancreas cancer cells.Positive nodes are considered a poor prognostic indicator; (b)Performance status, which consists of a five point scale (0, 1, 2, 3, 4)that reflects the general health of the patients, with 0 being the mostfavorable status and 4 being the least favorable; (c) CA19-9, which is ablood biomarker of pancreas cancer cells that serves as a measure oftumor burden. Higher CA19-9 levels are associated with poorer clinicaloutcomes; (d) Tumor size in centimeters, with larger size generallyassociated with poorer outcomes; and (e) Tumor stage, which ranges fromStage I through IV and is defined based on a standardized scoring systemthat consists of primary tumor size, extent of local invasion, extent ofinvolvement of regional lymph nodes and systemic spread of the canceraway from the primary tumor.

The primary endpoint for this clinical trial was recurrence-freesurvival. Secondary endpoints included overall survival, immuneresponses and biomarkers of disease burden, such as CA19-9. To date,GI-4000 in combination with adjuvant gemcitabine has shown evidence of aclinically meaningful effect on survival in Ras-mutation positive R1pancreas cancer subjects, including: (a) 2.6 month improvement in medianOS (17.2 months compared to 14.6 months); an 18% relative improvement;(b) 5.0 month improvement in median OS for GI-4000 immune responders(19.6 months compared to 14.6 months); a 34% relative improvement; (c)16% advantage in one-year survival (72% vs. 56%); a 30% relativeimprovement; and (d) 1 month improvement in median RFS (9.6 months forGI-4000/gemcitabine vs. 8.5 months); a 13% relative advantage. Inaddition, GI-4000 was immunogenic and well tolerated in R1 subjects: (a)7/15 (47%) in the GI-4000/gem arm vs. 1/12 (8%) in the placebo/gem armhad Ras mutation specific T cell response; and (b) GI-4000 has been welltolerated to date with no evidence of significant novel toxicities.Additional results were observed following the development of apredictive mass spectral method of the invention, which will bedescribed in detail below.

Predictive Mass Spectral Methods

An example of the mass-spectral methods of this disclosure will bedescribed below in detail in conjunction with a study of samples in theGI-4000+gemcitabine Phase 2b clinical trial in pancreas cancer describedabove. Some, but not all patients receiving the combination ofGI-4000+gemcitabine experienced a substantial improvement in RFS and OSas compared to those patients that received gemcitabine and placebo. Aclassifier was developed to predict in advance of treatment whether apatient is a member of the class of patient that is likely to benefit(“Slow” in the following discussion), or conversely is not likely tobenefit (“Quick” in the following discussion). FIGS. 2A and 2B areKaplan-Meier plots of recurrence-free survival (RFS) and overallsurvival (OS), of patients positive for a proteomic signature indicatingthey are likely to benefit from GI-4000 in combination with gemcitabine.FIG. 2A shows RFS by treatment group (GI-4000 vs. placebo) for subjectswith the late recurrence (“Slow”) proteomic signature, whereas FIG. 2Bshows OS by treatment group for subjects with the late recurrenceproteomic signature. The plots illustrate the ability of a massspectrometry method of this disclosure to identify patients which arelikely to benefit from the combination of GI-4000 and gemcitabine in thetreatment of pancreatic cancer. Further Kaplan-Meier plots from ourstudy showing the ability of our method to identify patients in thetreatment arm that did or did not obtain benefit from the combination ofGI-4000 and gemcitabine will be discussed in conjunction with FIGS. 3and 4 below.

In our study, samples suitable for generation of mass spectra wereavailable from 90 patients enrolled in GI-4000-02, described in detailabove. The samples were derived from blood, and in this particular casewere plasma that had been obtained from whole blood by a priordensity-based separation method. Whole blood (in sodium heparin glasstubes) was received at GlobeImmune laboratories from clinical trialsites by overnight delivery at room temperature and was processed within30 hr. of collection. To separate peripheral blood mononuclear cells(PBMCs), blood was diluted approximately 1:1 with Dulbecco's phosphatebuffered saline (D-PBS; Gibco/InVitrogen catalog #14190-250), layeredonto a Ficoll-Hypaque gradient in Leucosep™ tubes (Greiner) andcentrifuged at 1000×g for 10 minutes at ambient temperature. Beforecells were harvested from the gradients, the plasma diluted 1:1 withD-PBS was aspirated off and frozen at minus 80° C. Prior to use in themass spectra methods of the invention, the samples were thawed,aliquoted and then refrozen once before use.

Spectra were generated from the samples using the standarddilute-and-shoot (DNS) method and the “DeepMALDI” method, described inpending U.S. Provisional Application Ser. No. 61/652,394 filed May 29,2012, incorporated by reference herein, and described in further detailbelow.

We performed the VeriStrat test as described in U.S. Pat. No. 7,736,905and the previously-cited patent literature of Biodesix, Inc. on thedilute-and-shoot spectra, but found that the classifier did not yielduseful information. Few VeriStrat Poor samples were identified and nosignificant differences were found between VeriStrat Good and Poorpatients in either treatment arm.

Hence, a new classifier with a new training set needed to be defined. Wespeculated that VeriStrat features might be useful, because we believethat these are related to the host's immune and inflammatory response tothe presence of cancer (See US patent application publication2011/0208433). Indeed, it was discovered that mass spectral features inthe spectra used in the VeriStrat test (see Tables 3 and 4, below) couldbe used for classification in the pancreatic cancer study provided thatthe classifier training set was properly defined and the spectralpre-processing procedures changed, as described below. This discovery ofthe classifier design and training set will be described in thefollowing section.

Classifier Design

The starting point for designing a classifier to split patients intothose with better and worse prognosis on the GI-4000 treatment was todefine a training set of patients with better and worse recurrence-freesurvival (RFS). Based on the distribution of times of RFS, it wasdecided to define patients with quick recurrence (“Quick”) as thosepatients recurring before 276 days and patients with slow recurrence(“Slow”) as those patients without a recurrence event before 500 days.This gave a training set of 20 patients in the “Quick” group and 14patients in the “Slow” group, with 9 patients with intermediate RFStimes. (Aside: In fact there were 21 patients with RFS event before 276days, but the spectrum for one of these patients was missed whenstarting the project, so this patient was not initially included in thetraining set for the “dilute-and-shoot” and 150,000 shot DeepMALDIanalysis, and was only used when the classifier was applied to the wholecohort. For the 500,000 shot DeepMALDI analysis, this patient wasincluded in the training set and one patient with long recurrence timewas excluded, as it was determined that the plasma sample for thispatient had been taken during treatment.) It would be possible toproduce similar results by taking different cutoff points to define the“Slow” and “Quick” groups or to define them by quick and slow times todeath.

Having defined the training set of “Quick” and “Slow” groups, the massspectra to be compared were pre-processed using the methods of U.S. Pat.No. 7,736,905, including background subtraction, partial ion currentnormalization, and spectral alignment. The details of the pre-processingare different for the Dilute-and-Shoot spectra and the DeepMALDIspectra, although the general procedure is similar. First the backgroundis estimated and subtracted from the spectra. The spectra are normalizedto partial ion current. The regions used in calculating partial ioncurrent can be chosen in various ways, as long as they exclude thestrong and most variable peaks in the mass spectra. In the exampledilute-and-shoot classifier for which results are given below, theregions used for partial ion current calculation and normalization were3 kDa-11.4 kDa, 13 kDa-15 kDa and 16.1 kDa-30 kDa, but other choicescould be made. For example, for the 500,000 shot DeepMALDI spectra-basedclassifiers the regions used for partial ion current normalization were4.9 kDa-6.54 kDa, 12 kDa-13.5 kDa and 18 kDa-27 kDa. The noise in thespectra is estimated. Once the peaks are detected in the spectra, thespectra can be aligned using a set of alignment points. A set ofalignment points can be compiled by choosing a subset of the peaksdetected in the spectra that are common to most of the spectra to bealigned or can be chosen beforehand from peaks that are known to existin most spectra from prior experience. In the case of thedilute-and-shoot classifier shown below, the following alignment pointswere selected from prior experience. These were peaks at the followingm/z positions: 6434.5, 6632.1, 11686.9, 12864.8, 15131.1, 15871.5, and28102.5. It is worth noting that, if the DeepMALDI methods for obtainingmass spectral data from the samples (see explanation below) are used,other features in the spectra could be used for partial ion currentnormalization and spectral alignment and pre-processing methods forbackground subtraction more suitable for the DeepMALDI spectra may beused. For example, in the 500,000 shot DeepMALDI spectra-basedclassifiers presented below, the following alignment points were used:3315, 4153, 4457, 4710, 4855, 5289, 6431, 6629, 6835, 7561, 7931, 8202,8807, 8912, 9707, 12856, 13735, 14031, 14134, 15117, 15856, 17366,21046, 27890, 28019, 28067, and 28228. It should be noted, however, thatother choices of number and location of alignment points are possiblefor both methods of spectral acquisition.

Pre-processing the spectra renders them comparable with one another andthey can then be used to make a classifier, based on features that aredefined from external consideration, or the groups of spectra can becompared to determine features that are differentially expressed betweenthe groups, a subset of these features can then be selected and aclassifier built using this set of features. One of the classifiers withresults shown below was constructed using features determined fromexternal considerations.

As it is believed that the mass spectral features used in the VeriStratclassifier (see Tables 3 and 4 below) are correlated with inflammatoryprocesses linked to the host response to the presence of the tumor (seeour prior U.S. patent application Ser. No. 12/932,295 filed Feb. 22,2011, incorporated by reference herein) and that this is related to theresponse of the immune system to the tumor, it was of interest to tryusing the eight VeriStrat features with a new reference set of spectradefined from patients' recurrence times following GI-4000 treatment. Theresults are shown in FIG. 3, described below. However, other classifierscould be constructed using features found to differentiate between thetwo reference set groups during their comparison. For dilute-and-shootspectra these features include one or more of the following:

TABLE 1 m/Z center of feature m/Z left edge of feature m/Z right edge offeature 5841.168 5831.035 5851.302 6433.537 6427.831 6439.244 8765.4328757.666 8773.197 9669.883 9618.946 9720.82 11442.47 11428.45 11456.4911474.93 11460.88 11488.99 11529.59 11518.95 11540.22 11696.79 11650.4311743.15 11900.24 11878.99 11921.49 12865.48 12856.36 12874.6One or more of the features of Table 1 could be used in combination withfeatures in Tables 2, 3 and 5 for use in classifier.

For 150,000 shot “DeepMALDI” spectra, the features useful forclassification include the following:

TABLE 2 Peak # m/z center m/z left edge m/z right edge 1 3039.5293037.053 3042.005 2 3366.615 3358.219 3375.011 3 3432.149 3412.7773451.52 4 3473.153 3453.337 3492.969 5 3552.474 3543.957 3560.992 63680.091 3672.995 3687.187 7 3842.836 3836.58 3849.092 8 4203.8384193.707 4213.969 9 5180.126 5176.524 5183.728 10 5291.206 5287.735294.682 11 5700.687 5689.381 5711.994 12 5843.129 5840 5846.258 135860.729 5851.114 5870.344 14 5866.576 5862.227 5870.926 15 6008.8286004.271 6013.384 16 6192.594 6182.541 6202.648 17 6298.994 6294.6556303.333 18 6873.363 6870.128 6876.598 19 6903.79 6894.561 6913.018 206971.019 6967.629 6974.408 21 6985.879 6982.461 6989.296 22 6995.0126991.486 6998.538 23 7009.091 7002.267 7015.914 24 7023.426 7019.4317027.422 25 7035.244 7031.664 7038.825 26 7045.484 7041.962 7049.005 277057.527 7050.655 7064.399 28 7074.719 7070.399 7079.039 29 7150.5397136.65 7164.429 30 7245.544 7237.996 7253.093 31 7301.145 7297.0117305.279 32 7783.506 7778.087 7788.925 33 8361.169 8355.605 8366.734 348476.978 8470.468 8483.489 35 8767.557 8761.746 8773.368 36 9362.5259353.951 9371.098 37 9671.94 9664.199 9679.681 38 9759.032 9751.0819766.982 39 9788.134 9772.707 9803.561 40 9871.152 9861.387 9880.918 4111302.64 11295.53 11309.76 42 10485.61 10471.48 10499.73 43 10776.7210762.23 10791.21 44 11475.86 11468.27 11483.45 45 11494.79 11487.3111502.27 46 11529.86 11522.63 11537.1 47 11555.31 11541.01 11569.61 4811655.18 11616.93 11693.44 49 11709.95 11701.41 11718.49 50 11761.9411724.72 11799.16 51 11858.02 11814.24 11901.79 52 11909.8 11902.0111917.58 53 11939.01 11929.83 11948.2 54 12348.32 12339.42 12357.22 5512866.6 12858.57 12874.62 56 13072.62 13064.42 13080.81 57 13090.9113082.14 13099.68 58 13360.65 13351.7 13369.59 59 13807.33 13786.4713828.18 60 13913.92 13897.53 13930.32 61 14043.98 14035.87 14052.09 6214092.28 14084.11 14100.46 63 14125.28 14117.13 14133.43 64 14148.1514139.99 14156.32 65 14197.84 14181.99 14213.69 66 14258.28 14249.8814266.69 67 14429.84 14414.32 14445.35 68 14384.69 14357.6 14411.78 6914530.4 14520.1 14540.7 70 18801.94 18784.71 18819.18 71 18861.8818844.22 18879.55 72 18904.03 18884.67 18923.4 73 19860.06 19771.0919949.03 74 21710.89 21683.49 21738.29 75 22998.95 22839.61 23158.29 7628314 28282.63 28345.37 77 28518.76 28486.77 28550.75For 500,000 shot DeepMALDI spectra, the features useful forclassification include the following:

TABLE 5 m/z center m/z left edge m/z right edge 1 3463.350 3459.4003467.300 2 3609.960 3605.630 3614.280 3 3679.430 3674.080 3684.770 43840.800 3834.810 3846.780 5 3892.750 3889.050 3896.440 6 4077.7004072.100 4083.300 7 4492.020 4485.400 4498.640 8 4597.040 4592.7104601.370 9 5405.660 5400.820 5410.500 10 5555.430 5549.440 5561.410 115582.800 5578.980 5586.620 12 5635.590 5631.640 5639.530 13 5706.6105701.520 5711.700 14 5762.000 5758.050 5765.940 15 5840.660 5836.5805844.730 16 5873.770 5870.200 5877.330 17 5890.570 5885.730 5895.410 186107.260 6102.290 6112.220 19 6129.670 6125.720 6133.610 20 6328.5706323.730 6333.410 21 6892.580 6888.250 6896.900 22 6951.990 6948.0406955.930 23 6981.660 6977.580 6985.730 24 7004.170 6999.330 7009.000 257054.210 7048.220 7060.190 26 7153.620 7148.910 7158.330 27 7295.3107289.320 7301.290 28 7421.920 7417.970 7425.870 29 8357.210 8351.7308362.680 30 8762.290 8756.430 8768.140 31 8992.440 8987.350 8997.530 329199.780 9193.160 9206.400 33 9794.720 9791.280 9798.160 34 10000.0209993.530 10006.510 35 10018.610 10013.260 10023.960 36 10090.63010083.880 10097.370 37 10174.760 10168.520 10181.000 38 10200.61010191.950 10209.270 39 10657.440 10651.960 10662.910 40 10713.84010704.160 10723.510 41 10912.750 10905.490 10920.000 42 11402.14011396.410 11407.870 43 11432.410 11425.280 11439.540 44 11466.16011459.660 11472.650 45 11488.190 11481.820 11494.550 46 11520.58011514.720 11526.430 47 11543.880 11538.400 11549.350 48 11563.08011556.070 11570.080 49 11620.250 11613.120 11627.380 50 11676.22011670.870 11681.570 51 11699.260 11694.040 11704.480 52 11723.20011716.450 11729.950 53 11744.210 11739.370 11749.050 54 11775.74011767.330 11784.140 55 11821.600 11814.600 11828.600 56 11839.68011833.950 11845.410 57 11882.560 11877.850 11887.270 58 11900.77011894.660 11906.880 59 12401.800 12392.760 12410.840 60 12975.74012968.730 12982.740 61 13104.010 13098.150 13109.860 62 13145.13013138.380 13151.880 63 13399.430 13388.090 13410.760 64 13475.49013468.100 13482.870 65 13622.780 13616.670 13628.890 66 13648.07013642.590 13653.540 67 13677.100 13670.860 13683.340 68 13735.49013731.670 13739.310 69 13781.970 13774.960 13788.970 70 13800.22013794.870 13805.570 71 14007.100 14002.010 14012.190 72 14030.53014023.400 14037.660 73 14057.140 14051.410 14062.870 74 14078.92014073.060 14084.770 75 14096.180 14091.340 14101.020 76 14112.22014107.890 14116.550 77 14137.690 14129.540 14145.840 78 14186.02014178.130 14193.910 79 14244.340 14232.110 14256.560 80 14280.73014272.960 14288.490 81 14408.640 14403.290 14413.980 82 14423.03014418.310 14427.740 83 14436.400 14431.050 14441.740 84 14518.12014508.310 14527.920 85 14537.850 14531.740 14543.960 86 14653.49014645.820 14661.150 87 14715.700 14706.780 14724.610 88 15618.16015597.660 15638.660 89 17443.590 17430.700 17456.470 90 18734.93018724.840 18745.010 91 21675.550 21649.880 21701.220 92 22987.93022971.630 23004.220 93 23020.930 23009.110 23032.740 94 28038.09027994.740 28081.430 95 28231.950 28203.250 28260.650 96 28438.91028393.220 28484.590 97 28804.560 28750.090 28859.030Note that improvements to normalization of spectra may reveal stillfurther differentiating peaks, hence the above lists are not consideredexhaustive. Again, the precise m/z location is subject to slight shiftdepending on spectral alignment during pre-processing.

A. Dilute-and-Shoot Spectra-Based Classifiers

The plots of FIGS. 3A-3F show the performance of classifiers built usingthe “Slow” and “Quick” definitions for the reference set (“Slow”=norecurrence event before 500 days, “Quick”=recurrence before 276 days),the VeriStrat feature definitions (Tables 3 and 4), and dilute-and-shootpre-processed spectra. The classifier was applied both to the spectra ofthe GI-4000 (treatment) arm and to the 46 spectra from the placebo(control) arm. The classifier generated class labels of Quick or Slowfor the spectra based on a K-nearest neighbor classification algorithm(see U.S. Pat. No. 7,736,905), with FIGS. 3A-3B showing theclassification with K=1, FIGS. 3C-3D showing the classification with K=3and FIGS. 3E and 3F showing the classification with K=5.

From these results shown in FIGS. 3A-3F, we see that it is possible toseparate the treatment arm (GI-4000+gemcitabine) into two groups,“Quick” and “Slow”, where the “Quick” group has significantly worseoutcomes, in terms of both RFS and OS, than the “Slow” group. Incontrast, the control arm (gemcitabine+placebo) has similar RFS in both“Quick” and “Slow” groups. There is a treatment benefit in RFS in favorof GI-4000 in the “Slow” group. The case of treatment effect in OS isdifficult to decipher and it may be that the treatment benefit from theaddition of GI-4000 in the “Slow” group is diluted by treatmentsreceived after recurrence. Analysis of OS is also complicated bycensoring of events in 38% of the cohort.

Note that the ‘Quick’ treatment arm is lower than the control armsindicating that the ‘Quick’ patients, did not benefit from treatmentwith GI-4000 and gemcitabine. Therefore, the classifier provides theability to predict those patients that are not likely to benefit fromtreatments stimulating an immune response.

B. 150,000 Shot DeepMALDI Based Classifiers

The performance of a classifier based on pre-processed DeepMALDI spectra(see description below) and features selected from a comparison of thetraining set groups “Quick” and “Slow” as defined above, is shown inFIGS. 4A and 4B, with FIG. 4A showing the ability of the classifier toseparate Quick and Slow patients in RFS in the treatment arm but not inthe control arm, and FIG. 4B showing the ability of the classifier toseparate Quick and Slow patients in OS in the treatment arms but not inthe control arm. The features used in the classifier were chosen toapproximate the eight features in Table 4. Significant separation wasobserved between “Quick” and “Slow” groups in the treatment arm, but notin the control arm for both RFS and OS. Although not significantlydifferent, the “Slow” group shows a trend to better outcome, especiallyRFS, on GI-4000 treatment compared with the control arm of placebo.

FIGS. 4C-4D, 4E-4F, and 4G-4H, are Kaplan-Meier plots of RFS and OS forthree additional classifiers using DeepMALDI mass spectrometry of thesamples and subsets of the 77 features for DeepMALDI that are listed inTable 2 above. All three classifiers used the reference set of 20spectra from patients with recurrence times before 276 days (“Quick”)and 14 spectra from patients with no recurrence event or censoringbefore 500 days (“Slow”), the same pre-processing, optimized forDeepMALDI spectra, and K=5 in the K-nearest neighbor classificationalgorithm.

The classifier of FIGS. 4C and 4D used a subset of features from thelist of 77 candidate features of Table 2 above. We sorted the 77features by p value and used the 20 features with the lowest p valuesdescribing their relative expression difference:

m/Z center of feature m/Z left edge of feature m/Z right edge of feature3842.836 3836.5801 3849.0918 5860.7288 5851.1136 5870.3441 6903.78956894.5605 6913.0184 7023.4264 7019.4307 7027.422 7074.7189 7070.39897079.0389 7301.1449 7297.0108 7305.279 9362.5245 9353.9508 9371.09839671.9403 9664.1994 9679.6812 9759.0315 9751.0808 9766.9822 10776.721410762.229 10791.2138 11709.9483 11701.4091 11718.4875 11761.938311724.7165 11799.1602 11858.0166 11814.2429 11901.7903 13090.90913082.1414 13099.6766 14043.9806 14035.8701 14052.091 14125.279814117.1271 14133.4325 14148.1548 14139.9896 14156.3199 14197.841214181.9925 14213.6898 14258.2834 14249.8779 14266.689 18801.943718784.7057 18819.1817FIGS. 4E and 4F show Kaplan-Meier plots of RFS and OS for a thirdDeepMALDI classifier. In this example, we used features from the list ofspectra (Table 2) that were in the regions of VeriStrat features (Tables3 and 4) or strongly correlated with them.

m/Z center of feature m/Z left edge of feature m/Z right edge of feature5843.1286 5839.9995 5846.2576 5860.7288 5851.1136 5870.3441 11475.858311468.2665 11483.4501 11494.7897 11487.3141 11502.2653 11529.861511522.6267 11537.0962 11555.3129 11541.0141 11569.6118 11655.181811616.9251 11693.4385 11709.9483 11701.4091 11718.4875 11761.938311724.7165 11799.1602 11858.0166 11814.2429 11901.7903 11909.798511902.0132 11917.5837 11939.0138 11929.8302 11948.1974 22998.94822839.6075 23158.2885The results for the fourth DeepMALDI classifier are shown in FIGS.4G-4H. For this classifier, we used a subset of the features (Table 2)whose expression levels between groups were correlated in the oppositeway to those in the classifier of FIGS. 4E-4F, and which are not relatedto VeriStrat features.

m/Z center of feature m/Z left edge of feature m/Z right edge of feature7009.0908 7002.2674 7015.9142 7023.4264 7019.4307 7027.422 7035.2447031.6636 7038.8245 7074.7189 7070.3989 7079.0389 14043.9806 14035.870114052.091 14092.2825 14084.1062 14100.4588 14125.2798 14117.127114133.4325 14148.1548 14139.9896 14156.3199 14197.8412 14181.992514213.6898Note that the DeepMALDI classifiers of FIGS. 4A-4H clearly separate theQuick and Slow patients in the treatment arm while showing little or noseparation in the control arm, and thus perform similarly to the “diluteand shoot” classifiers of FIG. 3A-3F.

In interpreting the results of FIGS. 3A-3F and 4A-4H, it should be notedthat the results above will tend to be over-estimates of separationbetween “Quick” and “Slow” groups because the reference set of theclassifier is used in the analysis. Unfortunately, a validation set wasnot yet available with which to test the classifier performance in anindependent way. Hence, to provide an alternative assessment ofclassifier performance, a cross-validation analysis was carried out, asdescribed in the “cross-validation of classifier” section below.

C. 500,000 Shot DeepMALDI Spectra-Based Classifiers

Classifiers able to separate “Quick” and “Slow” patients in RFS well inthe treatment arm, but not in the control arm could also be constructedusing 500,000 shot DeepMALDI spectra. The DeepMALDI spectra werepre-processed identically for all classifiers presented in this sectionand the training set for these classifiers was again the “Quick” and“Slow” recurrence groups defined previously. At the time of thisDeepMALDI analysis updated survival data were available and so theperformance analysis of these classifiers made use of updated datarelative to those presented in sections A and B. The sets ofdifferentiating features used in each classifier were subsets of the 97features in Table 5 above. The K neighbors chosen for the K-nearestneighbor classification algorithm was optimized for each classifier.FIGS. 13A-13L are Kaplan-Meier plots of RFS and OS showing theperformance of 6 classifiers built using 500,000 shot DeepMALDI spectraand subsets of the 97 features listed in Table 5.

The classifier of FIGS. 13A and 13B used a subset of 42 features fromthe list in Table 5. They were selected to include both featurescontained in the m/z regions of the VeriStrat features (Tables 3 and 4)and also additional features selected based on low univariate p valuesfor differentiating between ‘Slow’ and ‘Quick’ groups in the trainingset. For this classifier, K=3 was found to be optimal and the center(m/z) of the features used are listed in the first column of Table 6.

TABLE 6 FIG. FIG. FIG. FIG. FIG. 13A-13B 13C-13D 13E-13F 13G-13H FIG.13I-13J 13K-13L 4492 4492 5636 5762 5841 5841 5891 6893 6893 6893 70047004 7295 7295 7422 7422 7422 8357 8357 8357 8762 8762 8762 8762 89928992 9795 9795 10000 10000 10175 10175 11402 11432 11466 11466 1146611466 11466 11488 11521 11521 11521 11521 11544 11563 11620 11620 1162011620 11676 11676 11699 11699 11723 11723 11723 11744 11776 11822 1188311883 11901 12976 13104 13145 13145 13623 13623 13648 13648 13648 1364813648 13648 13677 13677 13735 13735 13735 13735 13735 13782 13782 1378213782 13782 14007 14007 14057 14057 14057 14057 14057 14096 14096 1411214112 14138 14138 14244 14244 14281 14281 14423 14423 14423 14423 1442314436 14436 14436 14538 14538 14538 14538 14538 14716 14716 17444 1873522988 22988 22988 22988 22988 23021 28038 28038 28038 28038 28038 2823228232 28232 28232 28232 28439 28439 28805 28805 28805

The classifier of FIGS. 13C and 13D used a subset of 32 features fromthe list in Table 5 (center of feature m/z given), selected on the basisof univariate p values for differentiating between ‘Slow’ and ‘Quick’groups in the training set and on the ratio of amplitudes of featurevalues between ‘Slow’ and ‘Quick’ groups. This classifier used K=3 andthe features listed in the second column of Table 6. Although 19features are common to this classifier and the previous one, thisclassifier also contains 13 features not used in the previous classifierand yet gives similar performance in terms of the Kaplan-Meier plots.

The classifier of FIGS. 13E and 13F was developed using similar criteriato that of the previous classifier and used K=5. The features used arelisted in the third column of Table 6. The classifier has 23 of its 25features in common with the classifier of FIGS. 13A and 13B and morethan half in common with the classifier of FIGS. 13C and 13D. Despitethe similarity in the features chosen for the classifier, theperformance in the Kaplan-Meier plots would indicate that thisclassifier is more prognostic of outcome, rather than predictive oftreatment effect from GI-4000.

The classifier of FIGS. 13G and 13H used a subset of 13 of the featuresof the classifier of FIGS. 13E and 13F, listed in the fourth column ofTable 6, and K=5; the Kaplan-Meier plots indicate a performance moresimilar to the first two DeepMALDI classifiers (FIGS. 13A-D).

The classifier of FIGS. 13I and 13J was constructed using no features inthe m/z regions of the VeriStrat features of Table 3. However, it can beseen that it has similar performance in terms of Kaplan-Meier plots asthe classifiers which do include features from the m/z regions ofVeriStrat features. The features used are listed in the fifth column ofTable 6 and for this classifier K=7.

The classifier of FIGS. 13K and 13L uses only eight features, listed inthe last column of Table 6, and K=3. Four of the eight features were notused in any of the other classifiers. Still, the performance as assessedby the Kaplan-Meier plots is not markedly different from the otherclassifiers tested.

Note that most of these DeepMALDI classifiers also clearly separate the‘Quick’ and ‘Slow’ classified patients in the treatment arm, whileshowing little separation in the control arm, and thus perform similarlyto the “dilute-and-shoot” classifiers and those based on DeepMALDI withfewer shots.

Cross-Validation of Classifier

A. General Formulation and its Application to the “Dilute-and-Shoot”Classifier

A cross-validation of the “dilute and shoot” classifier shown in FIGS.3A-3F and described above was done by following the procedure outlinedin FIG. 5.

In the procedure of FIG. 5, at step 100 the features (peaks or m/zranges) used for classification and the pre-processing steps (backgroundsubtraction, normalization and alignment) were fixed. Then, steps 102,104, 105 and 106 were performed in an iterative fashion indicated by theloop 108. In step 102, 10 spectra were randomly chosen to leave out fortesting classifier performance. In step 104, a reference set of spectrawere selected from the remaining 34 spectra using the same time torecurrence (TTR) criteria, namely “Quick” defined as recurrence before276 days and “Slow” defined as above (no recurrence before 500 days). Atstep 105, a value of K in a K nearest neighbor classification algorithmwas chosen. At step 106, the classifier performance was evaluated, interms of Hazard ratios (HR) and medians on a “test set” of spectra. Thetest set of spectra are spectra in the treatment arm not included in thereference set (step 104), i.e. the 10 spectra omitted at step 102 plusany other spectra from patients with a TTR between 276 and 500 days. Theprocess was repeated (108) many times (70 in this example).

It will be appreciated that the procedure of FIG. 5 is of generalapplicability, with appropriate selection of the reference and test setsgiven the particular study under consideration.

This cross-validation of FIG. 5 will tend to provide a lower bound tothe performance of the classifier as it under-estimates performance inthe following ways:

1. The reference sets of the cross-validation classifiers are smallerthan those of the original classifier, which can impact performance.2. The test set of samples not included in the reference set, whilelarger than the 10 intermediate recurrence spectra before, is stillsmall. This can lead to large variability in calculated statistics andin some cases, when group sizes are very small, these statistics can bemeaningless.3. The test set is not representative of the treatment arm cohort as awhole. It contains a higher proportion of patients with intermediaterecurrence times. This imbalance makes comparison of the test setresults with other groups outside the test set (e.g. control arm)difficult.

Despite these limitations, the cross-validation analysis of classifiersbuilt using the definitions of “Quick” and “Slow” groups and VeriStratfeatures yielded some useful insights.

FIG. 6 shows the distribution of hazard ratios between “Quick” and“Slow” groups calculated for the 70 realizations in the cross-validationanalysis for the control arm, the whole treatment arm, and the test set(treatment arm excluding the classifier reference set). While allclassifiers produced a HR close to 1 for the control arm, the median HRfor the test set was 3.1, close to that for the whole treatment arm.

FIGS. 7A and 7B show the distributions of median RFS in “Quick” and“Slow” groups in the test set in the cross-validation analysis. In FIG.7A, the median for the “Slow” group is centered around 382 days, whilethe median for the “Quick” group is centered around 274 days. FIG. 7Bshows the distribution of the difference in medians between “Quick” and“Slow” groups in the test set. The difference between medians iscentered around 111 days, a clinically meaningful difference, with veryfew realizations showing a smaller median for the “Slow” group than the“Quick” group. Although comparison of test set and control arm iscomplicated by the imbalance in distribution of recurrence times betweenthe two groups, observation of the medians in the “Quick” and “Slow”groups within the control group and the test set show that that of the“Slow” Test Set lies predominantly above those of the “Quick” and “Slow”control groups, which in turn lie above that of the “Quick” Test Set.See FIG. 8, which is a plot of the distribution of medians of “Quick”and “Slow” groups in the Control Arm and the Test Set in thecross-validation analysis.

In addition, the distribution of difference in medians between the testset and control arm for the “Slow” group, indicates that in nearly allrealizations, the median RFS for the test set was greater than that ofthe control arm, despite the imbalance in the populations, see FIG. 9,which is a plot of the distribution of difference in medians betweentest set and control arm for the “Slow” group. The median difference inmedian RFS was around 60 days, again a meaningful clinical difference.

Cross-validation analysis for OS was hampered by having data censored inover a third of the total cohort in the clinical data initiallyavailable.

Another result of cross-validation analysis was the determination of thedependence of the ratio of Slow to Quick classifications on the choiceof K used in the K-nearest neighbor classifier. See FIG. 10A, which is aplot of the distribution of the ratio of Slow to Quick classificationsin the control arm for different values of K used in classifier. FIG.10B is a plot of the distributions of hazard ratios calculated for thetest set of the cross-validation analyses for various values of K. Whenwe did the cross-validation, we chose a K of 3, 5, or 7 in each of the70 iterations (FIG. 5, step 105). We had roughly ⅓ of each, so there atleast 20 values in each of the distributions K=3, 5, and 7. To see whathappened for K=1, we also re-ran 19 iterations of the cross-validationusing K=1. The hazard ratio of Slow to Quick was found to be largest inthe test set for K=5. FIG. 10B also plots the distribution of hazardratios obtained for the control arm for all 70 iterations of thecross-validation analysis. FIG. 10B demonstrates that it does not makemuch difference what value of K is used within the control arm, and thedistribution is quite narrow. FIG. 10B, when considered together withFIG. 10A, demonstrates that multiple choices of K are possible in aK-nearest neighbor classification algorithm, but that K=5 is a probablya preferred choice.

B. Cross-Validation of 150,000 Shot DeepMALDI Spectra-Based Classifiers

Application of the same cross-validation methods to the four 150,000shot DeepMALDI spectra-based classifiers presented earlier show that twoof them had superior performance, in terms of ability to predictrelative treatment benefit from the addition of GI-4000. FIG. 14 showsthe distribution of hazard ratios between patients classified as ‘Quick’and ‘Slow’ for RFS for the four classifiers from FIGS. 4A-4H in both thecontrol arm and the test set of the cross-validation. While the medianhazard ratios observed for the classifiers from FIGS. 4C-4F are similarin test set and control arms, those observed for the other 2 classifiersare greater in the test set than in the control arm, supporting theperformance evaluation from the Kaplan-Meier plots showing greaterseparation between ‘Quick’ and ‘Slow’ groups in RFS within the treatmentarm than the control arm, and supporting the predictive power of theclassifiers for the addition of GI-4000 to the gemcitabine controlregimen.

C. Cross-Validation of 500,000 Shot DeepMALDI Spectra-Based Classifiers

The identical cross-validation method was also applied to the six500,000 shot DeepMALDI spectra-based classifiers presented earlier andassessed in FIGS. 13A-M. The distributions of the hazard ratios for eachclassifier in both the control arm and the test set of thecross-validation are shown in FIG. 15. This shows that apparentdifferences in performance in Kaplan-Meier plots showing the wholetreatment and control arms are not always maintained when performance isassessed by cross-validation methods. In addition, it is furtherevidence that it is possible to construct many different classifiers,using different sets of features, which have similar performancecharacteristics, even in cross-validation.

Note that the training set may have unequal numbers of members of the“Quick” and “Slow” classes. To address the question of unequal groupsizes, the relative reference group sizes we selected were partially aresult of the distribution of recurrence times that we had in thetreatment arm. There happened to be many patients with recurrence timesbetween 250 and 275 days, probably because it coincided with a plannedMRI/CT assessment. So, there did not seem to be an appropriate place tosplit the group between those times. However, we decided that it wasbetter take a larger early recurrence group anyway and so we preferredto have more in this group. A K-nearest neighbor classificationalgorithm can be adjusted to take into account different group sizes inthe reference set, so it is not, in principle, a problem to have unequalgroup sizes in the training set.

Practical, Useful Tests

As noted throughout this disclosure, practical useful tests follow fromthe discoveries of this disclosure. One aspect is that the testingmethod of the invention identifies whether a particular cancer patientis a member of a group of cancer patients that are likely, or notlikely, to benefit from administration of a yeast-based immune responsegenerating therapy either alone or in addition to other therapies. Yetanother aspect is that the testing method of the invention identifieswhether a particular cancer patient is a member of a group of cancerpatients that are likely, or not likely, to benefit from administrationof yeast-based immunotherapy for mutated Ras-positive cancer, such asGI-4000, either alone or in addition to other therapies. Thisidentification can be made in advance of treatment.

In one example the method includes the steps of: a) obtaining ablood-derived sample from the patient; b) obtaining a mass-spectrum ofthe blood-based sample with the aid of a mass spectrometer; c) in aprogrammed computer, performing predefined pre-processing steps on themass spectrum, obtaining integrated intensity values of selectedfeatures in the spectrum over predefined m/z ranges after thepre-processing steps are performed, and comparing the integratedintensity values with a training set comprising class-labeled spectrafrom other cancer patients and classifying the mass spectrum with aclass label. The class label assigned to the spectrum is used to predictwhether the patient is likely or not likely to benefit from treatment inthe form of administration of a yeast-based immune response generatingtherapy either alone or in addition to other therapies.

In another example the method includes the steps of: a) obtaining ablood-derived sample from the patient; b) obtaining a mass-spectrum ofthe blood-based sample with the aid of a mass spectrometer; c) in aprogrammed computer, performing predefined pre-processing steps on themass spectrum, obtaining integrated intensity values of selectedfeatures in the spectrum over predefined m/z ranges after thepre-processing steps are performed, and comparing the integratedintensity values with a training set comprising class-labeled spectrafrom other cancer patients and classifying the mass spectrum with aclass label. The class label assigned to the spectrum is used to predictwhether the patient is likely or not likely to benefit from treatment inthe form of administration of a yeast-based immunotherapy for mutatedRas-positive cancer, either alone or in addition to other therapies.

The test is illustrated in flow chart form in FIG. 11 as a process 300.

At step 302, a blood-derived sample is obtained from the patient. Thesample in this example is plasma, after some processing steps on thesample (e.g., plasma obtained from whole blood by a priordensity-separation method). In one embodiment, the blood-derived samplesare separated into three aliquots and the mass spectrometry andsubsequent steps 304, 306 (including sub-steps 308, 310 and 312), 314,316 and 318 are performed independently on each of the aliquots. Thenumber of aliquots can vary, for example there may be 4, 5 or 10aliquots, and each aliquot is subject to the subsequent processingsteps.

At step 304, the sample (aliquot) is subject to mass spectrometry. Apreferred method of mass spectrometry is matrix assisted laserdesorption ionization (MALDI) time of flight (TOF) mass spectrometry,but other methods are possible, including the so-called “DeepMALDI”method of mass spectrometry disclosed in pending U.S. patent provisionalapplication 61/652,394 filed May 29, 2012, the content of which isincorporated by reference herein (see description below). Massspectrometry produces mass spectra consisting of data points thatrepresent intensity values at a multitude of mass/charge (m/z) values,as is conventional in the art. In one example embodiment, the samplesare thawed and centrifuged at 1500 rpm for five minutes at four degreesCelsius. Further, the samples may be diluted 1:10, or 1:5, in MilliQwater. Diluted samples may be spotted in randomly allocated positions ona MALDI plate in triplicate (i.e., on three different MALDI targets or“spots” as they are known in the art). After 0.75 ul of diluted sampleis spotted on a MALDI plate, 0.75 ul of 35 mg/ml sinapinic acid (in 50%acetonitrile and 0.1% trifluoroacetic acid (TFA)) may be added and mixedby pipetting up and down five times. Plates may be allowed to dry atroom temperature. It should be understood that other techniques andprocedures may be utilized for preparing and processing samples inaccordance with the principles of the present invention.

Mass spectra may be acquired for positive ions in linear mode using aVoyager DE-PRO or DE-STR MALDI TOF mass spectrometer with automated ormanual collection of the spectra. (Of course, other MALDI TOFinstruments could be used, e.g., instruments of Bruker Corporation).Seventy five or one hundred spectra are collected from seven or fivepositions within each MALDI spot in order to generate an average of2,000 spectra for each sample specimen. Spectra are externallycalibrated using a mixture of protein standards (Insulin (bovine),thioredoxin (E. coli), and Apomyglobin (equine)).

Note that the DeepMALDI methods may be used in step 304, see descriptionbelow, either over one MALDI plate spot or over several MALDI platespots.

At step 306, the spectra obtained in step 304 are subject to one or morepre-defined pre-processing steps. The pre-processing steps 306 areimplemented in a general purpose computer using software instructionsthat operate on the mass spectral data obtained in step 304. Thepre-processing steps 306 include background subtraction (step 308),normalization (step 310) and alignment (step 312). The step ofbackground subtraction preferably involves generating a robust,asymmetrical estimate of background in the spectrum and subtracts thebackground from the spectrum. Step 308 uses the background subtractiontechniques described in U.S. Pat. No. 7,736,905, which is incorporatedby reference herein. The normalization step 310 involves a normalizationof the background subtracted spectrum. The normalization can take theform of a partial ion current normalization, or a total ion currentnormalization, as described in U.S. Pat. No. 7,736,905. Step 312 asdescribed in U.S. Pat. No. 7,736,905 aligns the normalized, backgroundsubtracted spectrum to a predefined mass scale, which can be obtainedfrom investigation of the spectra in the training set used by theclassifier. The preprocessing steps are also described in some detail inthe above discussion of the GI-4000+gemcitabine clinical study. However,the specifics of the pre-processing, e.g., features or spectral regionsused for partial ion current normalization and alignment, may vary.

Once the pre-processing steps 306 are performed, the process 300proceeds to step 314 of obtaining integrated intensities in the spectrumover predefined m/z ranges. The normalized and background subtractedintensity values may be integrated over these m/z ranges. Thisintegrated value (i.e., the sum of intensities within the correspondingpredefined m/z range) is assigned to a feature. Predefined m/z rangesmay be defined as the interval around the average m/z position of thecorresponding feature with a width corresponding to the peak width atthis m/z position. This step is also disclosed in further detail in U.S.Pat. No. 7,736,905.

At step 314, in one possible embodiment the integrated values ofintensities in the spectrum are obtained at one or more of the followingm/z ranges:

TABLE 3  5732 to 5795  5811 to 5875  6398 to 6469 11376 to 11515 11459to 11599 11614 to 11756 11687 to 11831 11830 to 11976 12375 to 1252912502 to 12656 23183 to 23525 23279 to 23622 and 65902 to 67502.

In one embodiment, values are obtained up to eight m/z ranges centeredat or encompassing the peaks listed in Table 4 below. The significance,and methods of discovery of these peaks, is explained in the U.S. Pat.No. 7,736,905, and in U.S. application Ser. No. 12/932,295 filed Feb.22, 2011, published as US 2011/0208433, the contents of which areincorporated by reference herein. In practice the above widths (ranges)or peak positions in Tables 3 and 4 may vary slightly, e.g., due tovariation in how spectral alignment is performed. It has been furthernoted that using the “DeepMALDI” technique (see description below) manyfeatures are revealed in the spectrum which could be used forclassification. For dilute and shoot mass spectrometry one or more ofthe peaks of Table 1 could be used for classification, or combinationsof the features of Table 1 and Tables 3 and 4. It has been further notedthat using the “DeepMALDI” technique many features are revealed in thespectrum, combinations of which could be used for classification, seeTables 2, 5 and 6 and the examples of FIGS. 4A-4H described above.

At step 316, the values obtained at step 314 are supplied to aclassifier, which in the illustrated embodiment is a K-nearest neighbor(KNN) classifier. The classifier makes use of a training set of classlabeled spectra from a multitude of other patients. The training setwill include class-labeled spectra from patients that either benefittedor did not benefit from immune response generating therapies, such asyeast-based immunotherapy for cancer, which may be yeast-basedimmunotherapy for mutated Ras-positive cancer, either alone or incombination with other anti-cancer therapy. For example, the trainingset in the GI-4000 study described in the working example above includedclass-labeled spectra that were in the “Quick” and “Slow” time torecurrence groups. The class labels assigned to such spectra would takethe form of “Quick”, “Slow”, or the equivalent, such as for example“benefitted”, “non-responder”, “good”, “poor”, etc. The application ofthe KNN classification algorithm to the values at 314 and the trainingset is essentially a distance calculation and majority vote algorithmfrom comparison of integrated intensity values with predefined spectralfeatures in a multidimensional feature space, as explained in U.S. Pat.No. 7,736,905. Other classifiers can be used, including a probabilisticKNN classifier, support vector machine, or other classifier.

At step 318, the classifier produces a label for the spectrum, e.g.,“Quick” or “Slow”. The method can be performed with a single aliquot, orwith the sample separated into three aliquots, in which steps 304-318are performed in parallel on the three separate aliquots from a givenpatient sample (or whatever number of aliquots is used). At step 320, acheck is made to determine whether all the aliquots produce the sameclass label. If not, an undefined (or Indeterminate) result is returnedas indicated at step 322. If all aliquots produce the same label, thelabel is reported as indicated at step 324.

As described in this document, the class label reported at step 324 isthen used to guide the treatment of the patient. For example, thosepancreatic cancer patients labeled “Quick” in accordance with theclassification step are predicted as being unlikely to benefit fromtreatment from immune response generating therapies, such as yeast-basedimmunotherapy for cancer, which may be yeast-based immunotherapy formutated Ras-positive cancer, either alone or in combination with otheranti-cancer agents including gemcitabine. As another example, if theclass label for the spectrum of the pancreatic cancer patient afterclassification is identified as “Slow” in accordance with the test, thenthe patient is predicted as likely to benefit from immune responsegenerating therapies, such as yeast-based immunotherapy for cancer,which may be yeast-based immunotherapy for mutated Ras-positive cancer,either alone or in combination with gemcitabine, and the patientproceeds to be treated by administration with the immune responsegenerating therapy, such as yeast-based immunotherapy for cancer, whichmay be yeast-based immunotherapy for mutated Ras-positive cancer, aloneor in combination with gemcitabine.

It will be understood that steps 306, 314, 316 and 318 are typicallyperformed in a programmed general purpose computer using software codingthe pre-processing steps 306, the obtaining of integrated intensityvalues in step 314, the application of the KNN classification algorithmin step 316 and the generation of the class label in step 318. Thetraining set of class labeled spectra used in step 316 is stored inmemory in the computer or in a memory accessible to the computer.

The method and programmed computer may be advantageously implemented ata laboratory test processing center as described in U.S. Pat. No.7,736,905.

TABLE 4 Peaks used in classification. Peak number m/z 1 5843 2 11445 311529 4 11685 5 11759 6 11903 7 12452 8 12579Note: the m/z values of the peaks identified in Table 4 may be subjectto slight shifting to higher or lower m/z values depending on thespectral alignment process which is used to align all the spectra usedin the training set, and in aligning the test spectrum inpre-processing.

FIG. 12 is a schematic block diagram of a laboratory test processingsystem 400 which may be used to practice the methods of this disclosure.The system 400 may be implemented in a laboratory functioning as alaboratory test processing center for a multitude of patient samples,e.g., in a test service provider business. The system 400 receives ablood-derived sample 402, which may be whole blood, plasma, serum, orplasma after performance of other processing steps, e.g. plasma obtainedfrom whole blood by a prior density-based separation method. The sampleis diluted and aliquoted using procedures described above onto one ormore spots of a MALDI-TOF pate 404 which is then inserted into aMALDI-TOF mass spectrometer 406. The mass spectrometer generates a massspectrum 408, which is in the form of data pairs (m/z position,intensity) as is conventional. The mass spectral data is then stored indigital form in a database or machine readable memory 410. The memory410 is accessible a general purpose computer 414, e.g., via a local areanetwork, the details of which are not important. The memory 410 furtherstores the class-labeled spectra of a training set 412. The computer 410implements software instructions for performing the pre-processing stepson the spectrum 408 (background subtraction, normalization andalignment) and code for executing a classification algorithm (e.g.K-nearest neighbor) with respect to the spectrum after pre-processingand using the training set data 412. The computer then generates a classlabel for the spectrum 408 as explained in FIG. 11, which is used toguide treatment as disclosed herein.

The test system 400 can receive mass spectral data from a remoteMALDI-TOF instrument 420 via a computer network 422 and perform steps304-324 of FIG. 11. The MALDI-TOF instrument could be associated with aremote clinic, hospital, or laboratory, which may or may not beaffiliated with the entity that is implementing the classificationcomputer 414, however to ensure standardization and reproducibilitynormally the same entity that performs classification and generation ofthe class label will also be performing the mass spectrometry of thepatient sample.

“DeepMALDI” Methods for Obtaining Mass Spectra

In MALDI (matrix assisted laser desorption ionization) TOF(time-of-flight) mass spectrometry, a sample/matrix mixture is placed ona defined location (“spot”, or “sample spot” herein) on a metal plate,known as a MALDI plate. A laser beam is directed onto a location on thespot for a very brief instant (known as a “shot”), causing desorptionand ionization of molecules or other components of the sample. Thesample components “fly” to an ion detector. The instrument measures massto charge ratio (m/z) and relative intensity of the components(molecules) in the sample in the form of a mass spectrum.

Typically, in a MALDI-TOF measurement, there are several hundred shotsapplied to each spot on the MALDI plate and the resulting spectra (oneper shot) are summed or averaged to produce an overall mass spectrum foreach spot.

The conventional wisdom, at least in the area of MALDI-TOF massspectrometry of complex biological samples such as serum and plasma, isthat there is no need to subject the sample to more than roughly 1,000shots, otherwise the protein content is depleted, the laser and detectorin the instrument are subject to undue wear, and furthermore thatadditional shots would not reveal a significant amount of additionalinformation regarding the sample. Hence, it is common to use 500-1000shots per sample spot when obtaining mass spectrometry data from complexbiological samples, e.g., during biomarker discovery research.

In recent exploratory studies, we have discovered that collecting andaveraging many (more than 20,000, and typically 100,000 to 500,000)shots from the same MALDI spot or from the combination of accumulatedspectra from multiple spots of the same sample, leads to a reduction inthe relative level of noise vs. signal and that significant amount ofadditional spectral information from mass spectrometry of complexbiological samples is revealed. Moreover, a variety of standardparadigms using MALDI TOF MS appear to be plain wrong. First, it ispossible to run hundreds of thousands of shots on a single spot beforethe protein content on the spot is completely depleted. Second, thereduction of noise via averaging many shots leads to the appearance ofpreviously invisible peaks (i.e., peaks not apparent at 1,000 shots).Third, even previously visible peaks become better defined and allow formore reliable measurements of peak intensity and comparisons betweensamples when the sample is subject to a very large number of shots (muchmore than 1,000).

As one example, it has been discovered that subjecting a complexbiological sample such as a blood-based sample to a large number ofshots on a single spot (>20,000 and even 100,000 and 500,000 shots) inMALDI-TOF mass spectrometry leads to a reduction in the noise level andthe revealing of previously invisible peaks (i.e., peaks not apparent at2,000 shots). Moreover, this can be done without depletion of theprotein content of the sample. Additionally, previously visible peaksbecome better defined and allow for more reliable comparisons betweensamples. In standard spectra of blood-based samples (˜1,000 shots),typically 60-80 peaks are visible, whereas with 200,000 shots typically˜200-220 peaks are visible, with 500,000 shots typically ˜450-480 peaksare visible, and with 2,800,000 shots typically ˜760 peaks are visible.It should be understood that the number of peaks reported here isrelated to MALDI-TOF instrument settings and these numbers are only arough guide; depending on instrument settings and also on particularpeak detection algorithms (and of course the actual sample) more orfewer peaks will be visible. It also must be noted that the quality ofpeaks and the quantification of intensity (related to abundance) is alsobetter at least under some measure, as is illustrated in FIGS. 16A-16Ddiscussed below.

The peaks revealed at, for example, 200,000 shots are believed tocorrespond to minute quantities of intact (undigested) proteins presentin the serum sample. Using the techniques described herein and what isreferred to herein as the “DeepMALDI” approach (i.e., greater than20,000 shots per spot, and preferably roughly 250,000 to 750,000 or moreshots from the same spot or from the combination of multiple spots), itis believed that a very large number of proteins, and possibly at leasthalf of all the proteins present in a serum sample, can be detected in asemi-quantitative and reproducible fashion. The detection in asemi-quantitative fashion means that the measurements of intensity (peakheight, area under the peak) are related to the absolute abundance orconcentration of the proteins in the sample. The detection in areproducible fashion means that one can measure the same sample manytimes and one obtains the same results within some acceptablecoefficient of variation.

Obtaining more than 20,000 shots from a single MALDI spot can exceed theparameters of a modern MALDI-TOF machine; however we describe in thisdocument several methods of working around this limitation. Ideally, theMALDI-TOF instrument is designed to accommodate the “DeepMALDI” approachdescribed in this document, and several specific proposals for such amachine are offered in the following description, including automatedraster scanning features and capability of performing vastly more shotson a single spot.

The most pressing issue using many hundreds of thousands of shots from aMALDI sample spot is that in common spot preparation only some shotlocations within a spot yield sufficient ion current to contributesubstantially to signal in a combined spectrum. While initial resultshave been obtained using a labor intensive manual process to visuallyselect high ion yield locations within a given spot on a MALDI plate forlaser shots, and it is possible to proceed with this approach,automation of the process to select locations for laser shots ispossible and preferred for a high throughput implementation of theinvention (if not for the simple reason to not waste too many lasershots and degrade the laser life time substantially). An alternativeapproach is to improve the quality of MALDI spots in such a way thatmost randomly selected locations yield a high ion current. Bothapproaches are useful in the generation of DeepMALDI spectra.

Several methods for automation of spectral acquisition are described inthis section of this document. Automation of the acquisition may includedefining optimal movement patterns of the laser scanning of the spot ina raster fashion, and generation of a specified sequence for multipleraster scans at discrete X/Y coordinate locations within a spot toresult in say 750,000 or 3,000,000 shots from one or more spots. Forexample, spectra acquired from 250,000 shots per each of four samplespots can be combined into a 1,000,000 shot spectrum. As mentionedpreviously, hundreds of thousands of shots to millions of shotscollected on multiple spots containing the same sample can be averagedtogether to create one spectrum. One method of automation involves thegeneration of raster files for non-contiguous X/Y raster scanning of asample spot. Another method involves dividing the spot into a grid ofsub-spots (e.g., a 3×3 or 5×5 grid) and generating raster files forraster scanning at discrete X/Y coordinate locations of the sub-spots. Athird method is disclosed using image analysis techniques to identifyareas of interest containing relatively high concentrations of samplematerial for spectral acquisition (multiple shots) and/or those areaswhere the protein concentration is relatively low, and performingspectral acquisition in the areas with relatively high proteinconcentration.

An optimizing the process of sample application to the MALDI plate(“spotting”) to produce uniform, homogeneous crystals of thesample/matrix within a single spot is described below. This processfacilitates obtaining hundreds of thousands of shots from a single spoton the MALDI plate using automated methods.

This discovery and methods of this disclosure has many applications,including biomarker discovery, test development, substance testing,validation of existing tests, and hypothesis generation, e.g., inbiomarker discovery efforts. It is specifically contemplated that themethods are applicable to the predictive tests described elsewhere inthis document. The methods further enhance the potential of “dilute andshoot” methods in mass spectrometry research by its ability toreproducibly quantify the amount of many more proteins in a complexsample in a high throughput fashion, as compared to currentmethodologies.

Terminology used in this section of this document:

1. The term “transient spectrum” refers to the spectrum obtained from asingle packet of laser shots directed to a single location or x/yposition (each packet consists of a defined number of shots, e.g., 100,500, 800 shots, etc.) in a MALDI spot.

2. The term “location spectrum” refers to the cumulative sum of one ormore transient spectra while the laser shoots x times at the samelocation in a MALDI spot.

3. The term “spot spectrum” refers to the sum of all the locationspectra acquired during shooting over an entire, single MALDI spot. Thespot spectrum can be obtained using solely a summing operation to sumthe location spectra, or obtained using a summing operation afterperforming alignment and/or normalization operations (e.g., total ioncurrent normalization) on the location spectra. The spot spectrum can betypically obtained from 100,000 to 500,000 shots on the MALDI spot.Other options for obtaining the spot spectrum are possible, including a)performing background subtraction and normalization on the locationspectra and then summing; b) performing background subtraction andalignment on the location spectra and then summing; c) performingbackground subtraction, alignment, and normalization of the locationspectra and then summing. We have found that the best dynamic range isachieved by total ion current normalization (for details see U.S. Pat.No. 7,736,905) of location spectra and then summing; any backgroundsubtraction would be done in the spot spectrum.

4. The term “shot location” refers to a given location where the laserbeam intercepts a MALDI spot for shooting. In order to obtain 200,000 or500,000 shots per MALDI spot the laser beam is directed over the MALDIspot to a multitude (e.g., hundreds) of individual shot locations, e.g.,manually, or more preferably in an automated fashion using rasterscanning of the laser beam over the spot. As explained below, the rasterpattern design is important as it is generally undesirable to shootimmediately adjacent spot locations sequentially. Hence, the rasterpattern design sequentially selects shot locations that have somespatial separation and repeats the scanning over the entire MALDI spotin a spatially shifted manner to avoid sequential shooting ofimmediately adjacent locations in the spot.

5. The term “transient spectrum filtering” refers to a filtering orselection process that is used to either accept or reject a transientspectrum. As an example, in transient spectrum filtering, in order for atransient spectrum to be accepted a minimum number (e.g., 5) of peakswithin a predetermined m/z range must be present in the transientspectrum, and the signal to noise ratio in the transient spectrum mustbe above a specified threshold. Other filtering criteria can also beused, such as the total ion current of a spectrum needs to exceed acertain predefined threshold, or by using exclusion lists or inclusionlists as explained below. The spectrum filtering either accepts orrejects the transient spectrum in whole.

6. As used herein, the term “complex biological samples” is defined assamples containing hundreds or thousands of analytes, e.g., intactproteins, whose abundance is spread over a large dynamic range,typically many orders of magnitude. Examples of such complex biologicalsamples include blood or components thereof (serum or plasma), lymph,ductal fluids, cerebrospinal fluid, and expressed prostate serum. Suchcomplex biological samples could also consist of environmental or foodsamples.

An example of the spectral information revealed in the “DeepMALDI”method is shown in FIGS. 16A-16E. FIGS. 16A-16C are the plots of aselected mass/charge range (m/z ratio 7,000 to 8,000) showing threespectra of the same sample (serum) illustrating the increase indetectable peak content with increasing number of shots. The spectrum ofFIG. 16A resulted from 2,000 shots, the spectrum of FIG. 16B resultedfrom 100,000 shots, and the spectrum of FIG. 16C resulted from 500,000shots. Note particularly how the spectrum of FIG. 16A appearsessentially as noise and appears to contain little or no discerniblespectral information of interest. Contrast FIG. 16A with 16B in whichthe spectrum of FIG. 16B (spectrum obtained from 100,000 shots) containsmany individual peaks, e.g., the peaks identified at 10), that are notpresent in the spectrum of FIG. 16A. In the spectrum of FIG. 16C, thereare many peaks shown in the spectrum that are not shown in the otherspectra, or which might have been deemed as noise in the bottomspectrum. Comparing FIGS. 16C and 16B to FIG. 16A it is apparent that awealth of spectral information is revealed at 100,000 shots and 500,000shots that is not present in the spectrum of FIG. 16A (2,000 shots), andthat the noise level is reduced by the DeepMALDI method as demonstratedin FIGS. 16B and 16C.

The spectra of FIGS. 16B are 16C increase the sensitivity of the spectrato a dynamic range that can be specified and can allow one to correlatepeak intensity to abundance. It is possible to use peak intensity toanalyze a complex biological sample for presence of a molecule at agiven concentration. For example, in this method one would define themolecule of interest (of known mass) in the sample, dope the specimen toa target abundance level (molar concentrations, or ppm) and apply to aMALDI plate; perform a number of shots on the plate (e.g., more than100,000) until the molecule is reliably present in the spectrum (a peakat a known m/z position) at a particular abundance (intensity), andrecord the number of shots (“x”). This procedure to generate what isreferred to as a “reference spectrum” would be subject to routinequalification and standardization methods to ensure reliability, aswould be apparent to persons skilled in the art. Then, a sample ofinterest for testing would be subject to MALDI-TOF and x number ofshots. If the resulting spectrum revealed that the intensity of the peakat the known position corresponding to the molecule of interest was lessthan the intensity of the peak in the reference spectrum then theconcentration of the molecule of interest in the sample is less than theconcentration of the molecule in the sample used in generation of thereference spectrum. This approach could be used for multiple analytessimultaneously. Furthermore, multiple reference spectra could beobtained for the molecule of interest over a range of knownconcentrations at x shots and the test spectrum could be compared to thereference spectra to determine an approximate concentration of themolecule of interest in the test sample. This method can be used formany purposes, e.g., drug testing, e.g., of an athlete, testing ofmetabolite concentration, environmental sample testing, etc. Themolecule of interest could be a protein, e.g., metabolite, CancerAntigen (CA) 125, prostate-specific antigen (PSA), C-reactive protein,etc., in a mass range of approximately 1K Daltons to 50 K Daltons.

FIG. 16D is an illustration of the enormous dynamic range in a spectrumthat is revealed in the DeepMALDI approach. The inset in FIG. 16D is aportion of a spectrum in the m/z range between 7140 kDa and 7890 kDashowing the spectrum, and multitude of peaks 10, obtained at about˜500,000 shots. A background estimate (dashed line) is superimposed overthe spectra, which could be subtracted out to produce a backgroundsubtracted spectrum. Note that the spectrum information in the inset andin particular many of the peaks 10 are not visible in the main portionof FIG. 16D. In FIG. 16E, the spectrum is shown in the inset with the Yaxis amplified in order to show the additional spectral information andin particular intensity information for peaks in the region of m/zaround 9520 which are revealed with the DeepMALDI method but which arenot visible in a typical ˜1,000 shot spectrum.

FIG. 16A is a plan view of a MALDI-TOF target plate 12 containing 384sample spots or “spots” 14 arranged in a rectangular array. The spotsare identified by column numbers 1 . . . 24 and rows A . . . P, e.g.,the upper left spot is identified as A1. FIG. 16B is an enlarged view ofan individual sample spot P1 (14) on which is superimposed an X/Ycoordinate system 16 having an origin (0,0). The sample spot 14 is showndivided into a 5×5 rectangular grid 25 individual sub-spots 18. Therectangular grids 18 and location coordinate system 16 are used in anautomated raster scanning approach to acquire 100,000 or more shots fromthe spot as described in detail below.

It was initially noted that automated generation of a large number ofshots (>20,000) is not absolutely necessary and existing features incurrently available MALDI-TOF instruments could be used. In general, inthe present DeepMALDI technique, it is important to select locations ona MALDI spot that produce a high protein yield when exposed to a lasershot. The standard software in existing mass spectrometry instrumentsallows for moving over a spot using regular pre-defined paths, i.e.square pattern, hexagonal pattern, spiral pattern (from the center of aspot). Shot locations on a MALDI plate are defined in a process called‘teaching’, a part of the FlexControl™ (Bruker) mass spec controlsoftware present in an existing MALDI-TOF instrument of BrukerCorporation. (While mention is made herein occasionally to features of aBruker Corporation instrument, the inventive methods are of course notlimited to any particular instrument or instruments of a particularmanufacturer.)

An example of a MALDI spot containing a specimen/matrix mixture evenlydistributed within the spot is shown in FIG. 18. Mass spectrometryinstruments from Bruker Corporation include a built-in camera that showsareas of a MALDI spot; in manual selection one would pick brightlocations 30 to aim the laser at. Dark locations 32 should be avoided.Sometimes bright locations do not produce good yields, which may berelated to the presence of salt crystals. Over the process of shooting,areas in a spot can become depleted; hence dark areas (depleted areaswith low yield) need to be avoided. The manual approach would continueto acquire and display images of the spot over the course of shooting.

In the course of our preliminary experiments we found that it wasbecoming increasingly harder to find good locations as more and moreshots were used. This effect was also seen when the same spot was usedrepeatedly, e.g. adding a second half million shots following a previoushalf million shots. The second run did not result in as much a reductionof noise level in mass spectra as was expected. In fact, the resultingaveraged spectra may be of worse overall quality, possibly arising fromaveraging shots from too many empty locations. This might result in anacquisition bias towards early locations if using the eye alone toselect shot locations and accept or reject spectra and not usingtransient spectrum filtering, and such bias needs to be controlled. Ifone uses automated raster scanning and location spectrum filtering thisbias is eliminated.

However, to increase throughput, it is desirable to automate the processof location selection and obtain high numbers of shots from a givenspot. Several methods are described in the following section. Methodsdescribed below are capable of acquiring 750,000 shots from a samplelocated on three spots (250,000 shots per spot) in a MALDI plate in13-15 minutes, with the sample requirement of 3 microliters of serum.

Automation of Spectra Collection

While results have been obtained using a labor intensive manual processto visually select locations within a given spot on a MALDI plate formultiple shots to yield 100,000 or 500,000 shots per spot, and it ispossible to proceed with this approach, automation of the process toselect locations for laser shots is possible and several methods aredescribed in this document.

Automation of the acquisition may include defining optimal movementpatterns of the laser scanning of the spot in a raster fashion, andsequence generation for multiple raster scans at discrete X/Y locationswithin a spot to result in, for example, 100,000, 250,000 or 500,000shots from the sample spot. One method of automation involves thegeneration of raster files for non-contiguous X/Y raster scanning of asample spot. The raster pattern design is important, as it is generallyundesirable to shoot immediately adjacent spot locations sequentially.Hence the raster pattern design sequentially selects shot locations thathave some spatial separation and repeats the scanning over the entireMALDI spot in a spatially shifted manner to avoid sequential shooting ofimmediately adjacent locations in the spot and to select new shotlocations.

Another method involves dividing the spot into a grid of sub-spots(e.g., a 3×3 or 5×5 grid) (see FIG. 17B) and generating of rasterscanning files for raster scanning at discrete X/Y locations of thesub-spots.

A third method is disclosed using image analysis techniques to identifyareas of interest containing relatively high concentrations of samplematerial for spectral acquisition (multiple shots) and/or those areaswhere the sample (e.g., protein) concentration is relatively low, andavoiding spectral acquisition in areas of relatively low sample (e.g.,protein) concentration.

A. Raster Scanning of Non-Contiguous X-Y Coordinates

One method of automation of the process of obtaining a large number ofshots from a spot involves the generation of raster files fornon-contiguous X/Y raster scanning of a sample spot. This will bedescribed in conjunction with FIGS. 19 and 20.

FIG. 19 is an illustration of a raster scanning pattern 500 for use inobtaining 100,000 or more shots from the spot 14 of FIG. 18. The spot 14is raster scanned multiple times, e.g., 25 times in a sequentialfashion. The symbol sets 502 shown in FIG. 19 depict individual,discrete X/Y locations where the spot is scanned (shot) in a singleraster scan. The X/Y locations are defined according to a coordinatesystem shown in the Figure having an origin at the center (position0,0). During scanning, when the laser is directed to each location, thesample at that location can be subject to a great many shots, e.g., 700or 800 shots per position/location. One will note from the pattern shownin FIG. 19 that each raster scan consists of shooting at individual,discrete locations within the spot. The individual raster scans areimplemented sequentially thereby avoiding shooting immediately adjacentlocations in the spot. FIG. 20 shows the superposition of the rasterpatterns of FIG. 19 over the spot of FIG. 18.

A procedure for generation of 25 raster files with non-contiguous X/Ycoordinates for raster scanning as shown in FIG. 19 is described in theappendix to U.S. Provisional Application 61/652,394 filed May 29, 2012and the interested reader is directed to that document for furtherreference.

B. Use of Grids to Separate a Spot into Sub-Spots and Raster Scanning ofSub-Spots

An objective of this method is to automate the process of manuallyselecting locations/rasters on a sample spot (i.e. spot A1, spot A2,etc.) that result in “acceptable” spectra during data acquisition and todo this until several hundred thousand spectra have been added to thesum buffer. Summing up/averaging several hundred thousand spectraincreases the signal to noise ratio, and therefore allows for thedetection of significantly more peaks, as described previously.

As is the case with non-contiguous raster scanning described above, theuse of grids as described in this section works best when thesample/matrix mixture is substantially evenly and homogeneouslydistributed over the entire spot, as shown in FIG. 18. A presentlypreferred method for achieving this is described later in this documentfor dilute-and-shoot serum and sinapinic acid (matrix). Because of thiseven distribution, we can therefore acquire spectra from virtually alllocations/rasters on the sample spot, which eliminates the need for aprecursory evaluation of all locations/rasters for “acceptable” spectra.

Collecting several hundred thousand spectra on a sample spot can beachieved by defining a grid (FIG. 17B) that subdivides the spot 14 intosub-spots or grid elements 18, that covers the sample spot, andcollecting a defined number of spectra from each location/gridpoint/raster within each sub-spot 18 until the desired number of spectrahave been added to the sum buffer. Previous versions of the Brukersoftware only allowed for the summation of a maximum of 20,000 totalspectra per sample spot in automatic mode (FIG. 21).

To circumvent this limitation we initially defined a 5 by 5 grid area(FIGS. 17B, 16) that divides each sample spot into twenty-five 8×8 gridsor sub-spots 18 (FIG. 17B). A separate raster file is generated for eachgrid or sub-spot 18. The instrument is instructed to acquire 800 spectra(shots) at each location/raster within a grid 18 until 20,000 spectrahave been added to the (spectrum) sum buffer. At that time, theautomatic method will instruct the instrument to move to the next gridor sub-spot 18 and use the next raster file and generate another 20,000spectra. In practice, one designs 25 raster files, one for each sub-spot18, each of which is attached to a separate AutoExecute™ (Bruker) methodthat acquires data according to evaluation criteria setup within themethod.

This procedure permits acquisition of 500,000 shot spectra (20,000 shotspectra per grid×25 grids) in batches of 20,000 shots each usingBruker's Flexcontrol™ software tools without having to use imagingapplications such as flexImaging™ (Bruker). The result of this procedureis 25 spectra files for one sample spot each containing one summedspectrum composed of 20,000 shot spectra. These 25 spectra files canthen be summed to produce an overall spectrum for a single spot on aMALDI plate obtained from 500,000 shots, e.g., as shown in FIGS. 16C,16D and 16E.

The most recent version of Flexcontrol™ (Bruker) allows one toaccumulate a summed spectra from up to 500,000 shots. For example, inFIG. 21 the AutoExecute™ (Bruker) method editor allows the summation of20,000 shots in 800 shot steps (800 shots per location/raster).

However, one can only collect one summed spectra (sum of x transientspectra) per sample spot. To acquire several batches of summed spectrafrom a single sample spot, we had to make adjustments to existingsoftware features in the MS instrument. With these adjustments we canacquire spectra from one or several rasters that makes up a grid such asthe ones described above, and save each transient or location spectrumindividually. For instance, the instrument can be instructed to collectand save each 800 shot location spectra acquired at each raster (x,yposition) in the grid or sub-spot 18 in FIG. 17B without having to addto the sum buffer. The same process is repeated for all the sub-spotswithin the sample spots A1, A2, A3 etc. (e.g. 800 shot spectra can beacquired from 250 rasters per sample spot=200,000 shots per samplespot). The location spectra can be acquired with or without applyingspectrum filtering in AutoExecute™ (Bruker).

C. Image Analysis

One option for automation of spectral acquisition is image processingtechniques to identify spatial locations on a spot with high proteinyield/high sample concentration particularly in the situation where thesample is not spatially evenly distributed over the spot and instead isconcentrated in discrete areas. In one possible embodiment, the cameraincluded in the instrument is used to acquire an optical image of atraining spot. Then, mass spectra are acquired from a raster oflocations on the training spot. Resulting mass spectra are used, incombination with the optical image of the spot, to generate aclassification mechanism to detect, from the optical image, high yieldlocations of further spots prepared from a given sample preparation.This classification would then be applied to the actual sample spots.While this is an elegant solution, we encountered issues with capturingthe camera feed, and the repeatable calibration of locations from cameraimages to laser shot locations.

An alternative method is to investigate a spot using the massspectrometer directly in the form of a mass spectral imaging approach.The idea is to first run a preliminary scan and shoot a low number ofshots (dozens) at each location of a fine scale (square) pattern on aspot. Spectra will be collected for each of these raster locations, andthe total ion current, or ion current within some predefined range ofm/z, will be recorded for each location. A new raster file will begenerated based on the N highest intensity locations from thepreliminary scan run, and used in the final acquisition of mass spectra.This approach utilizes the Bruker FlexImaging™ software as the mostfeasible solution to generate multiple spectra in the mass spec imagingrun. Software analyzes these spectra, and generates a final raster scanpattern. While this method will likely be useful for standard dilute andshoot processes using sinapinic acid as a matrix, it might be suboptimalfor other matrices and for pre-fractionated sample sets (e.g. CLCCA, seeLeszyk, J. D. Evaluation of the new MALDI Matrix4-Chloro-a-Cyanocinnamic Acid, J. Biomolecular Techniques, 21:81-91(2010)), and other methods like NOG precipitation (Zhang N. et al.,Effects of common surfactants on protein digestion and matrix-assistedlaser desorption/ionization mass spectrometric analysis of the digestedpeptides using two-layer sample preparation. Rapid Commun. MassSpectrom. 18:889-896 (2004)). An important aspect of this alternativemethod is to find acquisition settings in the MS imaging part so as tonot generate too large files. A standard acquisition file is of theorder of one megabyte, and for a 400 by 400 raster scan (400 locations,400 shots per location) we generate 16,000 spectra. As the requirementsfor these spectra are not onerous at all, and we only need to estimatethe total ion current, we can work with low resolution settings. It maybe possible to directly obtain a list of usable locations from automaticspectral acquisition settings, i.e. getting a list of successful orfailed acquisitions. From our investigations it appears that it may bepossible to use mass filtering as part of the MS imaging package togenerate a list of locations (recognized via a file list) that passcertain criteria. While this will greatly help with the generation of aprototype workflow, it will need to be optimized via specializedsoftware to avoid a semi-manual process.

FIG. 22 shows a region of a MALDI spot using CLCCA as a matrix, wherethe high yield areas consist of linear structures and areas of low yieldare shown as dark areas. For these cases, where the matrix samplecrystallizes very unevenly, like shown in FIG. 20, the image analysisapproach seems most sensible. The image analysis identifies therelatively high yield areas (120, 122). The relatively low yield areas,such as the areas 124 on the lower left and the matrix area 126 areidentified by the image analysis software and are ignored duringshooting.

The image analysis software to identify high and low yield areas on aspot could take a variety of forms, and can be developed by personsskilled in the art. For example, the black and white image of the spot(FIG. 19) consists of an array of pixels, each having an 8 bit quantizedvalue, with 0 being black (no signal) and 255 being white (saturated).The filtering can be used to identify areas of relatively high yield,such as by identifying pixels with a pixel value greater than say 100being identified as “high yield” and pixels having a pixel value lowerthan 40 being identified as relatively “low yield”. The scanning thenproceeds to those areas of the sample spot in which the correspondingpixel has a value of 100 or more. It may also be possible to filter outspot locations in which the pixel value is 240-255 as such areas may bedetermined to have salt crystals or other properties that result in lowyield. Referring again to FIG. 22, the pixels for the crystallinestructures 120, 122 have pixel values falling in the range of 100-240and thus would be scanned whereas the black areas 124 and 126 would notbe. Morphological processing techniques could also be used to identifystructures such as the crystals 120 of FIG. 22. The image analysissoftware could include both morphological processing and filtering todetermine areas to scan. Additionally, the spot can change during thecourse of scanning (due to depletion of the sample) and the imageprocessing can be run during the scanning to optimize the shooting overthe course of generating 100,000 or more shots from a spot, and thoselocations of low sample concentration avoided during shooting.

FIG. 23 is a screen shot from a MALDI-TOF instrument showing the displayof the instrument workstation 130, including an image 132 of a spot 14,in this case spot F17 of the plate. The layout of the plate is shown at12′, with the spot F17 indicated at 14′. A group of spots 134 (D9 toF20) are selected for running in an automatic mode using the imageanalysis method described above.

FIG. 24 is another screen shot from the instrument. Current instrumentsallow the user to set evaluation regions to accept or reject transientspectra (using the Evaluation tab), set how many spectra to accumulateper spot (using the Accumulation tab) and “move” across the spot so thatthe laser can fire in a certain pattern (using the “Movement” tab,shown). The options include random walk or movement in pattern, e.g.,hexagon or spiral. The software also allows the user to keep firing thelaser and acquiring and adding to the total spectra according to suchparameters until spectra from 750 shots are collected from a shotlocation, and then move to the next shot location. One can set thenumber of tries before the shot location is considered a failed spot.The image analysis methods in which likely areas of low yield areidentified, and shooting in those areas avoided, helps in considerablyreducing or eliminating those failed judgments.

FIG. 25 shows an evaluation page where a mass range for accepting orrejecting transient spectra is selected, as indicated at 150. Duringacquisition, if a transient spectra does not have peaks in thepredefined range—in this case 5,000 to 18,000 Da, that pass thethreshold set (based on resolution, signal intensity or other factors),then it will be rejected. That is, the transient spectra will not beadded to the sum buffer to form the location spectrum (summing thespectra from all of the shots).

FIG. 26 shows an evaluation page where if there are specific peaks thatone does not want included in the evaluation one can make an exclusionlist and tag these peaks as “background peaks.” The software haspredefined “control lists” for matrices which define background peaks,or one can import a peak list.

Collection of Spectra from Multiple Spots

In general, one can extend the DeepMALDI technique to combining spectrafrom multiple spots. For example, one can obtain 500,000 shots of asample from each of the spots A1, A2, A3, A4 and A5 on a standard MALDIplate (See FIG. 17A), and combine (sum) the resulting spectra into oneoverall spectrum consisting of a sum of 2,500,000 spectra (shots). Apriori, there is no reason to believe that one could not combine spectrafrom multiple spots to reach extremely high number of shots, i.e., 100spots×1 million shots each could give us results from 100 million shots.There may be practical limits to this procedure, e.g., the laser mayfail too often.

Example of Collection of Spectra from Multiple Spots in DeepMALDI

In one example of this method, it is possible to collect spectra from 5million shots from multiple spots of the same serum on a MALDI plate,using manually or automatically generated rasters for scanning themultiple spots using the techniques described previously. In thismethod, it is preferred to obtain reproducibly homogenous spots of asingle sample on the MALDI plate. This can be achieved using the methodsdescribed herein.

1. Spotting Diluted Serum onto MALDI Target Plate.

Procedure:

Dilute serum 1:10 with HPLC grade water and vortex. Mix sample withmatrix (20 mg/ml sinapinic acid in 50% ACN/0.1% TFA) 1:1 (v/v) in a 0.5ml microfuge tube and vortex. Spot 4 μl of the matrix/sample mixtureonto one or more spots on the MALDI target.Thirty Six Spots (Locations) in the MALDI Plate were Used in thisExample:Tube 1: spotted on locations E13, E14, and E15 of MALDI plate (See FIG.2A)Tube 2: spotted on locations E16, E17, and E18Tube 3: spotted on locations E19, E20, and E21Tube 4: spotted on locations E22, E23, and E24Tube 5: spotted on locations F1, F2, and F3Tube 6: spotted on locations F4, F5, and F6Tube 7: spotted on locations F7, F8, and F9Tube 8: spotted on locations F10, F11, and F12Tube 9: spotted on locations F13, F14, and F15Tube 10: spotted on locations F16, F17, and F18Tube 11: spotted on locations F19, F20, and F21Tube 12: spotted on locations F22, F23, and F24Sample spots E13 to F18 (Tubes 1-10) were directly applied aftervortexing using the same pipette tip 3 times (3×4 ul of 15 ul in eachtube; while the last six samples spots F19-F24 (Tubes 11 and 12) wereapplied as in spots E13-F18, but also pipetted up and down on plate.Spots on MALDI plate were allowed to dry at ambient temperature byplacing target plate on bench-top.

Result:

For spots E13 to F17 (which were directly applied to plate with nofurther on-plate mixing) the third spot from each tube was clearly morehomogenous than the first two. Homogeneity was assessed visually: thirdspot is best, second spot is second best, first spot is the leasthomogenous, with the exception of E23 which is from second of threespots from tube 4, but looked more like the third spotting from eachtube than the second spottings.

Sample spots F18, F19, F20, F21, F23 and F24, which were mixed byvortexing in tube and pipetted up and down on plate, were fairly similarand had the same uniform appearance as the third spot in the set fromE13 to F17. F22 looked about the same as E23.

2. Acquisition of Spectrum from 5 Million Shots

Mass spectral data from approximately 312,500 shots per spot wasobtained from sixteen MALDI spots after the above procedure wasperformed:

E15, E18, E21, E23, E24, F3, F6, F9, F12, F15, F18, F19, F20, F21, F23and F24.

Using raster scanning files as described above, the spectra from theeach of the spots was summed to produce an overall spectra of the sampleobtained from approximately 5,000,000 shots.

Optimization of Sample Application to MALDI Plate (Spotting)

The sample application to the MALDI plate is optimized to providehomogenous and even distribution of the crystallized sample to eachsample spot on a MALDI plate, an example of which is shown in FIG. 15.Several experiments were performed as described below to find an optimumprocedure for supplying the sample mixture to a spot on the MALDI plate(“spotting”). These experiments are described in this section.

Initially, several different preparations with serum were prepared. 2 μlof matrix was spotted unless otherwise noted. Diluted sample and matrixmedium were mixed in a sample prep tube unless otherwise noted. We didnot spot more than 1 spot from a single prep tube unless otherwise notedas taking multiple aliquots out of the sample prep tube affectscrystallization.

Ground Steel Plate experiments were conducted which produced homogeneousspots. The procedures were as follows:

1. Diluted sample 1:10 (2 μl sample+18 μl of water), then mixed 1:1(v/v) with matrix (sinapinic acid 25 mg/ml) in 50% ACN/0.1% TFA andspotted 2 μl of matrix. This procedure did not produce good, homogeneouscrystals.

2. Primed matrix tip. Pipetted 2 μl of matrix into spotting tip and letit sit for 30 seconds. Diluted sample 1:10 (2 μl sample+18 μl of water),then mixed 1:1 (v/v) with matrix (sinapinic acid 25 mg/ml) in 50%ACN/0.1% TFA. Ejected excess matrix from pipette tip. Placed pipette tipin sample matrix mixture and pipetted up and down 3 times. Spotted 2 μlof sample matrix mixture without changing the tip. This procedure formedgood crystals that were homogeneous. Because this is a ground steelplate the sample matrix mixture doesn't spread out as much as on thepolished steel plate. The dried crystals that are left in the pipettetip might improve crystallization by acting as a seed for furthercrystal formation.

3. The effect of temperature on crystallization was studied. Dilutedsample 1:10 (2 μl sample+18 μl of water), then mixed 1:1 (v/v) withmatrix (sinapinic acid 25 mg/ml) in 50% ACN/0.1% TFA. Place sample in37° C. water bath for 5 minutes. Removed sample from water bath andspotted immediately. This procedure didn't produce good, homogeneouscrystals.

4. Repeated experiment 2. above but spotted 4 μl of sample mixtureinstead of 2 μl. This procedure formed good crystals that werehomogeneous. Spotting 4 μl fully covered the spot diameter and producegood crystals and data. This is the procedure currently consideredoptimal.

The procedures for spotting here are offered by way of example and notlimitation, and variation from the disclosed methods are of coursepossible. For example, one may mix the matrix and sample material in thetube and let it set for several minutes before spotting. It has beennoted that one gets more homogeneous crystals the more spots are madefrom the same tube using the same pipette tip. For example, one couldspot 10 spots from the same tube using the same tip and only collectdata on the last 5 or so spots; or alternatively one could discard thefirst five 4 μl aliquots from the tube before commencing spotting on aMALDI plate.

We have also found that following the procedure in 1 but using the samepipette tip to spot the same sample tube 10 times (2.5 μl per spot) ontoa polished steel target plate yields similar results (spectral quality).

Further Considerations

Technical Reproducibility

Technical reproducibility studies can be done, e.g. to run 1,000technical replicates in batches of 100 each day. One can studydependence on sample (spot) preparations (on or off plate), inparticular to see whether there are preparation methods that yield moreuniform ion-current yields, e.g. variations in sample dilution. One canalso monitor how the number of high-yield locations changes from spot tospot, and how to minimize variations in this. Monitoring and logging allacquisitions and preparations at a high level of granularity is goodpractice.

Sample to Sample Reproducibility

Similar issues of sample to sample reproducibility can be studied withrespect to sample to sample variations. New phenomena might occur: Itmay be that some samples are protein rich, and result in spots with morehigh-yield locations. It may be possible to obtain measures from somemanner of sample attributes (optical density and color), or standardizesample acquisition devices (e.g., for serum) to generate morereproducible procedures. One may use a combined sample set with asheterogeneous a source as possible to attempt to cover most variations.Such a set should be obtained from studying existing sets and matchingaccording to known sample collection and conditions, which makes stronguse of existing sample databases.

Sensitivity

Observing more peaks in the spectra raises the question what abundancerange we can see in this method, and what protein types are actuallyvisible. This deals with the ‘conventional wisdom’ that in MALDI MS ofcomplex samples one cannot observe lower abundance ions due to ‘ionsuppression’, the idea that ions from more abundant proteins suppressthe ion signal from less abundant proteins, therefore rendering the lessabundant proteins undetectable. This idea appears to be solely based onthe lack of observation of lower abundance ions. Indeed, our observationof an increase in peak content (see e.g., FIG. 16C) casts some doubtover this interpretation. Rather, it appears that one has to takeseriously the (semi) quantitative nature of MALDI MS. If one agrees thatprotein abundance spans a wide range over many orders of magnitude, thenone would expect that corresponding mass spectra would mimic thisbehavior by exhibiting a vast difference in peak height (or rather thearea under a peak). One would not expect to observe low abundanceproteins in MALDI spectra, not because they do not ionize, but ratherbecause the amplitude of peaks corresponding to low abundance proteinsshould be very low. As it is common practice in mass spectrometry tofocus on large peaks, and because lower abundance peaks would be ordersof magnitude smaller, it is not surprising that these peaks have notbeen observed before. This is not to say that phenomena like ionsuppression do not occur, or that ionization probability does not play arole, but to say that these phenomena do not entirely suppress peaksoriginating from low-abundance proteins, and that, if one looks for lowabundance protein peaks in the low intensity region of spectra, they doindeed become observable. The quest for covering a significantpercentage of the serum proteome can thus be viewed as a quest forextending the dynamic range of mass spectra. As with any other countingbased technique the simple solution to this problem is to increasestatistics by increasing the number of detected ions (per time-of-flightbin).

In order to get more confidence in this simple interpretation, whichruns counter to conventional wisdom, one may wish to establish thedynamic range of mass spectra and link it to abundance of proteins. Thisshould be done both from an analytical chemistry point of view,establishing sensitivity curves (as a function of m/z), as well asthrough the identification of proteins corresponding to some peaks andcomparative abundance measurements of these proteins via orthogonaltechniques like ELISAs.

Using Pre-Fractionated Samples

The methods of this disclosure can be used in combination withprecipitation methods for fractionating a sample, e.g. NOGprecipitation, de-lipidifying, and so on. The methods can also be usedwith other matrices like CLCCA. It is likely that these methods couldalso benefit greatly from the DeepMALDI approach. Our preliminary datausing sample pre-fractionation indicate that one does indeed seedifferent peaks, but the peak content was far from optimal. This mightbe expected as one purpose is to get rid of high abundance proteins.

In the past we attempted to use depletion and/or mass filtering toreduce the content of unwanted proteins like albumin and hemoglobin, butnone of these methods led to a total removal, and remnants of thesepeaks were still visible. Using the DeepMALDI approach described here ondepleted or mass filtered samples should yield better results, asreducing large peaks will also reduce the dynamic range necessary to seelower abundance proteins.

Obtain Sensible Choices of Spectral Acquisition Settings

In the autoExecute™ (Bruker) method, it is possible to define filteringsettings in order to only collect transient spectra that pass certaincriteria; in our case we want to only add those transient spectra(arising from <xx> number of shots) that have a total ion current largerthan an externally defined threshold. While this does not seem possiblein a simple manner, there are filter criteria in the processing methodtab that might be used for similar purposes. Alternatively, there mightbe parameters in the peak evaluation methods that we could tune for thispurpose. While this will not reduce the number of shots, it may overcomethe problem of shot bias towards earlier shots, i.e. not to acquiretransients consisting only of noise. The use of automated filteringoperations in summing transient spectra to generate location spectraavoids the problem of bias.

Increase Spot Size

Given the limitations arising from the size of the laser illumination aswell as from the minimal grid size for the pre-rastering step, it maywell be that there are not enough shot locations with sufficiention-yield on a standard spot. A simple way to address this would be toincrease the spot size. The FlexImaging™ (Bruker) software would supportthis very easily. There are also options of rectangular spotting areasused in MS imaging application that might be suitable for this purpose.An additional benefit of using larger spots would be that one does nothave to worry whether one can locate a similar number of decent shotlocations and generate spectra of similar quality from spot to spot.Sample volume does not appear to present an issue. If larger spots arepossible, it would reduce the logistics to deal with multiple spots forthe same acquisition, which may be necessary for high numbers of shots.

Still further considerations for “DeepMALDI” methodology are describedin U.S. provisional application Ser. No. 61/652,394 filed May 29, 2012and the interested reader is directed to that document.

Yeast-Based Immunotherapy and Therapeutic Methods of the Invention

One embodiment of this disclosure is directed to the use of yeast-basedimmunotherapy compositions designed to stimulate therapeutic immuneresponses against cancer antigens expressed by a tumor cell in a patientwith cancer. The method includes a step of administering a yeast-basedimmunotherapy composition for cancer (i.e., comprising a cancer antigen)to a subject that has a cancer expressing the cancer antigen, and whohas been identified or selected as likely to benefit from administrationof the composition by a test performed in accordance with any of themass spectral predictive methods of the invention as described herein.

More specifically, in one aspect, a method of treating a cancer patientwith yeast-based immunotherapy for cancer is described. The methodincludes the steps of: (a) conducting a test in accordance with any ofthe methods of predicting described herein, and if the class label forthe spectra indicates the patient is likely to benefit from yeast-basedimmunotherapy for cancer, (b) administering the yeast-basedimmunotherapy for cancer. In one aspect, the patient is additionallytreated with one or more additional anti-cancer therapies, either priorto, concurrently with, or after, treatment with the yeast-basedimmunotherapy for cancer. In one embodiment, the additional anti-cancertherapies include, but are not limited to, surgery (e.g., surgicalresection of a tumor), chemotherapy, radiation therapy, targeted cancertherapies (e.g., small molecule drugs or monoclonal antibody therapiesthat specifically target molecules involved in tumor growth andprogression), and palliative care, or any combination thereof.

In another aspect, this disclosure relates to a method of treating acancer patient with yeast-based immunotherapy for cancer. The methodincludes the step of administering yeast-based immunotherapy for cancerto a cancer patient that has been selected by a test in accordance withany of the predictive methods of the invention as described herein, inwhich the class label for the spectra indicates the patient is likely tobenefit from the yeast-based immunotherapy for cancer. In one aspect,the patient is additionally treated with one or more additionalanti-cancer therapies, either prior to, concurrently with, or after,treatment with the yeast-based immunotherapy for cancer. In oneembodiment, the additional anti-cancer therapies include, but are notlimited to, surgery (e.g., surgical resection of a tumor), chemotherapy,radiation therapy, targeted cancer therapies (e.g., small molecule drugsor monoclonal antibody therapies that specifically target moleculesinvolved in tumor growth and progression), and palliative care, or anycombination thereof.

In yet another aspect of this disclosure, a method of treating a cancerpatient with yeast-based immunotherapy for mutated Ras-positive canceris described. This method includes the steps of: (a) conducting a testin accordance with any of the methods of predicting described above, andif the class label for the spectra indicates the patient is likely tobenefit from yeast-based immunotherapy for mutated Ras-positive cancer,(b) administering the yeast-based immunotherapy for mutated Ras-positivecancer. In one aspect, the patient is additionally treated with one ormore additional anti-cancer therapies, either prior to, concurrentlywith, or after, treatment with the yeast-based immunotherapy for mutatedRas-positive cancer. In one embodiment, the yeast-based immunotherapyfor mutated Ras-positive cancer is a product in the series ofyeast-based immunotherapy products known as GI-4000, or the equivalent.In one aspect of this embodiment of the invention, the mutatedRas-positive cancer can include, but is not limited to, pancreas cancer,non-small cell lung cancer (NSCLC), colorectal cancer (CRC), endometrialcancers, ovarian cancers, melanoma and multiple myeloma. In one aspect,the cancer is pancreas cancer. In one embodiment, the additionalanti-cancer therapies include, but are not limited to, surgery (e.g.,surgical resection of a tumor), chemotherapy, radiation therapy,targeted cancer therapies (e.g., small molecule drugs or monoclonalantibody therapies that specifically target molecules involved in tumorgrowth and progression), and palliative care, or any combinationthereof. In one aspect, the yeast-based immunotherapy for mutatedRas-positive cancer is administered to the patient in conjunction withgemcitabine or the equivalent. In one embodiment, the patient is apancreas cancer patient and the therapy comprises a product in theseries of yeast-based immunotherapy products known as GI-4000 or theequivalent, either alone or in combination with gemcitabine. In oneaspect, the cancer patient's tumor has been surgically resected prior totreatment with the yeast-based immunotherapy composition.

In yet another aspect of this disclosure, a method of treating a cancerpatient with yeast-based immunotherapy for cancer is described. Themethod includes the step of administering a yeast-based immunotherapyfor mutated Ras-positive cancer to a cancer patient selected by a testin accordance with any of the predictive methods of the invention asdescribed herein in which the class label for the spectra indicates thepatient is likely to benefit from the yeast-based immunotherapy formutated Ras-positive cancer. In one aspect, the patient is additionallytreated with one or more additional anti-cancer therapies, either priorto, concurrently with, or after, treatment with the yeast-basedimmunotherapy for mutated Ras-positive cancer. In one embodiment, theyeast-based immunotherapy for mutated Ras-positive cancer is a productin the series of yeast-based immunotherapy products known as GI-4000, orthe equivalent. In one aspect of this embodiment of the invention, themutated Ras-positive cancer can include, but is not limited to, pancreascancer, non-small cell lung cancer (NSCLC), colorectal cancer (CRC),endometrial cancers, ovarian cancers, melanoma and multiple myeloma. Inone aspect, the cancer is pancreas cancer. In one embodiment, theadditional anti-cancer therapies include, but are not limited to,surgery (e.g., surgical resection of a tumor), chemotherapy, radiationtherapy, targeted cancer therapies (e.g., small molecule drugs ormonoclonal antibody therapies that specifically target moleculesinvolved in tumor growth and progression), and palliative care, or anycombination thereof. In one aspect, the yeast-based immunotherapy formutated Ras-positive cancer is administered to the patient inconjunction with gemcitabine or the equivalent. In one embodiment, thepatient is a pancreas cancer patient and the therapy comprises a productin the series of yeast-based immunotherapy products known as GI-4000 orthe equivalent, either alone or in combination with gemcitabine. In oneaspect, the cancer patient's tumor has been surgically resected prior totreatment with the yeast-based immunotherapy composition.

This disclosure uses of the term “yeast-based immunotherapy”, whichphrase may be used interchangeably with a “yeast-based immunotherapeuticcomposition”, “yeast-based immunotherapy product”, “yeast-basedimmunotherapy composition”, “yeast-based composition”, “yeast-basedimmunotherapeutic”, “yeast-based vaccine”, or derivatives of thesephrases). As used herein, a yeast-based immunotherapeutic compositionrefers to a composition that includes a yeast vehicle component and anantigen component that targets a disease or condition in a subject(i.e., a yeast-based immunotherapeutic composition for cancer includes ayeast vehicle component and a cancer antigen component that targets thecancer in a patient). A yeast-based immunotherapeutic compositioncomprising a mutated Ras antigen useful in the present invention targetsmutated Ras-positive tumors in a patient. This composition can bereferred to as a “yeast-Ras immunotherapy composition”, or a“yeast-based immunotherapeutic composition expressing Ras antigen”, or“yeast-based immunotherapy for mutated Ras-positive cancer”. Ayeast-based immunotherapy for mutated Ras-positive cancer includes, butis not limited to the series of yeast based immunotherapy products knownas “GI-4000”.

In conjunction with the yeast vehicle, the cancer antigens (e.g.,mutated Ras antigens) included in a yeast-based immunotherapy productare most typically expressed as recombinant proteins by the yeastvehicle (e.g., by an intact yeast or yeast spheroplast, which canoptionally be further processed to a yeast cytoplast, yeast ghost, oryeast membrane extract or fraction thereof), although it is anembodiment of the invention that one or more such cancer antigens areloaded into a yeast vehicle or are otherwise complexed with, attachedto, mixed with or administered with a yeast vehicle, to form acomposition useful in the present invention.

In one aspect of the invention, antigens useful in one or moreyeast-based immunotherapy compositions of the invention include anycancer or tumor-associated antigen. In one aspect, the antigen includesan antigen associated with a preneoplastic or hyperplastic state. Theantigen may also be associated with, or causative of cancer. Such anantigen may be a tumor-specific antigen, a tumor-associated antigen(TAA) or tissue-specific antigen, an epitope thereof, or an epitopeagonist thereof. Cancer antigens include, but are not limited to,antigens from any tumor or cancer, including, but not limited to,melanomas, squamous cell carcinoma, breast cancers, head and neckcarcinomas, thyroid carcinomas, soft tissue sarcomas, bone sarcomas,testicular cancers, prostatic cancers, ovarian cancers, bladder cancers,skin cancers, brain cancers, angiosarcomas, hemangiosarcomas, mast celltumors, leukemias, lymphomas, primary hepatic cancers, lung cancers,pancreatic cancers, gastrointestinal cancers (including colorectalcancers), renal cell carcinomas, hematopoietic neoplasias and metastaticcancers thereof.

Suitable cancer antigens include but are not limited to mutated Rasoncoprotein (see, e.g., U.S. Pat. Nos. 7,465,454 and 7,563,447),carcinoembryonic antigen (CEA) and epitopes thereof such as CAP-1,CAP-1-6D (GenBank Accession No. M29540 or Zaremba et al., 1997, CancerResearch 57:4570-4577), MART-1 (Kawakami et al, J. Exp. Med.180:347-352, 1994), MAGE-1 (U.S. Pat. No. 5,750,395), MAGE-3, GAGE (U.S.Pat. No. 5,648,226), GP-100 (Kawakami et al., Proc. Nat'l Acad. Sci. USA91:6458-6462, 1992), MUC-1 (e.g., Jerome et al., J. Immunol.,151:1654-1662 (1993)), MUC-2, normal and mutated p53 oncoproteins(Hollstein et al Nucleic Acids Res. 22:3551-3555, 1994), PSMA (prostatespecific membrane antigen; Israeli et al., Cancer Res. 53:227-230,1993), tyrosinase (Kwon et al PNAS 84:7473-7477, 1987), TRP-1 (gp75)(Cohen et al Nucleic Acid Res. 18:2807-2808, 1990; U.S. Pat. No.5,840,839), NY-ESO-1 (Chen et al PNAS 94: 1914-1918, 1997), TRP-2(Jackson et al., EMBO J, 11:527-535, 1992), TAG72, KSA, CA-125, PSA(prostate specific antigen; Xue et al., The Prostate, 30:73-78 (1997)),HER-2/neu/c-erb/B2, (U.S. Pat. No. 5,550,214), EGFR (epidermal growthfactor receptor; Harris et al., Breast Cancer Res. Treat, 29:1-2(1994)), hTERT, p′73, B-RAF (B-Raf proto-oncogeneserine/threonine-protein kinase; Sithanandam et al., (1990), Oncogene5(12):1775-80), adenomatous polyposis coli (APC), Myc, von Hippel-Lindauprotein (VHL), Rb-1, Rb-2, androgen receptor (AR), Smad4, MDR1 (alsoknown as P-glycoprotein), Flt-3, BRCA-1 (breast cancer 1; U.S. Pat. No.5,747,282), BRCA-2 (breast cancer 2; U.S. Pat. No. 5,747,282)), Bcr-Abl,pax3-fkhr, ews-fli-1, Brachyury (GenBank Accession Nos. NP_(—)003172.1or NM_(—)003181.2; Edwards et al., 1996, Genome Res. 6:226-233), HERV-H(human endogenous retrovirus H), HERV-K (human endogenous retrovirus K),TWIST (GenBank Accession Nos. NM_(—)000474 and NP_(—)000465), Mesothelin(Kojima et al., 1995, J. Biol. Chem. 270(37):21984-90; Chang and Pastan,1996, Proc. Natl. Acad. Sci. U.S.A. 93(1):136-40), NGEP (New GeneExpressed in Prostate; Bera et al., 2004, Proc. Natl. Acad. Sci. U.S.A.101(9):3059-3064; Cereda et al., 2010, Cancer Immunol. Immunother.59(1):63-71; GenBank Accession Nos. AAT40139 or AAT40140), modificationsof such antigens and tissue specific antigens, splice variants of suchantigens, and/or epitope agonists of such antigens. Other cancerantigens are known in the art. Other cancer antigens may also beidentified, isolated and cloned by methods known in the art such asthose disclosed in U.S. Pat. No. 4,514,506. Cancer antigens may alsoinclude one or more growth factors and splice variants of each.

In one aspect of the invention, the cancer antigen is carcinoembryonicantigen (CEA), a polypeptide comprising or consisting of epitopesthereof such as CAP-1, CAP-1-6D (GenBank Accession No. M29540 or Zarembaet al., 1997, Cancer Research 57:4570-4577), a modified CEA, a splicevariant of CEA, an epitope agonist of such CEA proteins, and/or a fusionprotein comprising at least one immunogenic domain of CEA or an agonistepitope thereof. In one aspect, the CEA is a modified CEA correspondingto the modified CEA having an amino acid sequence represented by SEQ IDNO:46 in U.S. Patent Publication No. US 2007_(—)0048860, published Mar.1, 2007, which is encoded by a nucleic acid sequence of SEQ ID NO:45 inthat publication.

In one aspect of the invention, the yeast-based immunotherapycomposition targets human Brachyury. Brachyury antigens and yeast-basedimmunotherapy compositions targeting brachyury have been described inPCT Publication No. 2012/125998, published Sep. 20, 2012.

In one aspect of the invention, the yeast-based immunotherapycomposition targets mucin-1 (MUC-1). MUC-1 antigens and yeast-basedimmunotherapy compositions targeting MUC-1 have been described in PCTPublication No. 2013/025972, published Feb. 21, 2013.

In one aspect of the invention, a yeast-based immunotherapy compositiontargets mutated Ras-positive cancers. ras is an oncogene in whichseveral mutations are known to occur at particular positions and beassociated with the development of one or more types of cancer.Therefore, a yeast-based immunotherapy product for mutated Ras-positivecancers includes at least one immunogenic domain of Ras containing anamino acid residue that is known to be mutated in certain cancers. Suchcancers include, but are not limited to, pancreas cancer, NSCLC,colorectal, endometrial and ovarian cancers, as well as melanoma andmultiple myeloma. In one aspect, a yeast-based immunotherapy product formutated Ras-positive cancers contains two, three, or more immunogenicdomains of Ras, wherein each domain contains one or more different Rasmutations known to occur in certain cancers, in order to cover severalor all known mutations that occur in Ras proteins. For example, in oneaspect of the invention, the Ras antigen used in the yeast-basedimmunotherapeutic composition comprises at least 5-9 contiguous aminoacid residues of a wild-type Ras protein containing amino acid positions12, 13, 59, 61, 73, 74, 75, 76, 77 and/or 78 relative to the wild-typeRas protein, wherein the amino acid residues at positions 12, 13, 59,61, 73, 74, 75, 76, 77 and/or 78 are mutated with respect to thewild-type Ras protein. In one aspect, the cancer antigen includes: (a) aprotein comprising at least from positions 4-20 or at least frompositions 8-16 of a wild-type Ras protein, except that the amino acidresidue at position 12 with respect to the wild-type Ras protein ismutated; (b) a protein comprising at least from positions 5-21 or atleast from positions 9-17 of a wild-type Ras protein, except that theamino acid residue at position 13 with respect to the wild-type Rasprotein is mutated; (c) a protein comprising at least from positions51-67 or at least from positions 55-63 of a wild-type Ras protein,except that the amino acid residue at position 59 with respect to thewild-type Ras protein is mutated; (d) a protein comprising at least frompositions 53-69 or at least from positions 57-65 of a wild-type Rasprotein, except that the amino acid residue at position 61 with respectto the wild-type Ras protein is mutated; (e) a protein comprising atleast from positions 65-81 or at least from positions 69-77 of awild-type Ras protein, except that the amino acid residue at position 73with respect to the wild-type Ras protein is mutated; (f) a proteincomprising at least from positions 66-82 or at least from positions70-78 of a wild-type Ras protein, except that the amino acid residue atposition 74 with respect to the wild-type Ras protein is mutated; (g) aprotein comprising at least from positions 67-83 or at least frompositions 71-79 of a wild-type Ras protein, except that the amino acidresidue at position 75 with respect to the wild-type Ras protein ismutated; (h) a protein comprising at least from positions 69-84 or atleast from positions 73-81 of a wild-type Ras protein, except that theamino acid residue at position 77 with respect to the wild-type Rasprotein is mutated; (i) a protein comprising at least from positions70-85 or at least from positions 74-82 of a wild-type Ras protein,except that the amino acid residue at position 78 with respect to thewild-type Ras protein is mutated; and/or (j) a protein comprising atleast from positions 68-84 or at least from positions 72-80 of awild-type Ras protein, except that the amino acid residue at position 76with respect to the wild-type Ras protein is mutated. It is noted thatthese positions correspond generally to K-Ras, N-Ras and H-Ras proteins,and to human and mouse sequences, as well as others, since human andmouse sequences are identical in these regions of the Ras protein andsince K-Ras, H-Ras and N-Ras are identical in these regions of the Rasprotein.

As used herein, the term “GI-4000” generally refers to a series ofyeast-based immunotherapy compositions (TARMOGEN® products), where eachyeast-based immunotherapy composition expresses one or more Rasmutations that target Ras mutations observed in human cancers. Suchmutations are associated with the development of tumors. GI-4000 as usedin the clinic and described in the Examples herein presently consists ofa series of four yeast-based immunotherapy product versions, denotedindividually as GI-4014, GI-4015, GI-4016 and GI-4020. Each version is aheat-inactivated, whole (intact) Saccharomyces cerevisiae yeastrecombinantly expressing a fusion protein containing a uniquecombination of three Ras mutations (one mutation at position 12 withrespect to the native Ras protein and two different mutations atposition 61 with respect to the native Ras protein), collectivelytargeting seven of the most common Ras mutations observed in humancancers (four different position 12 mutations and 3 different position61 mutations). In the GI-4000 clinical studies, each patient's tumor issequenced to identify the specific Ras mutation contained in thepatient's tumor and the corresponding yeast-Ras immunotherapy productcontaining the identified mutated protein is then administered. Eachproduct in the GI-4000 series is manufactured and vialed separately.

More particularly, each fusion protein expressed by a yeast-basedimmunotherapeutic composition in the series of GI-4000 productspresently used in the clinic (denoted individually as GI-4014, GI-4015,GI-4016 and GI-4020) has an amino acid sequence generally having thefollowing overall structure, from N- to C-terminus: (1) a two amino acidN-terminal peptide sequence (M-V), (2) an amino acid sequencecorresponding to positions 56-67 of Ras having a single amino acidsubstitution corresponding to position 61 of the native Ras protein, and(3) an amino acid sequence corresponding to positions 2-165 of Rashaving two single amino acid substitutions corresponding to positions 12and 61, respectively, of the native Ras protein. The specificcombination of the three amino acid substitutions in each fusion proteindistinguishes one fusion protein from the other. Other structures andorganization of immunogenic domains within yeast-based immunotherapyproducts targeting mutated Ras-positive cancers are possible, and aredescribed in detail in, e.g., U.S. Pat. No. 7,465,454.

The nucleotide and translated amino acid sequence for the constructencoding GI-4014 are represented by SEQ ID Nos:1 and 2, respectively.GI-4014 comprises the following Ras mutations: Q61L-G12V-Q61R. Thenucleotide and translated amino acid sequence for the construct encodingGI-4015 are represented by SEQ ID Nos:3 and 4, respectively. GI-4015comprises the following Ras mutations: Q61L-G12C-Q61R. The nucleotideand translated amino acid sequence for the construct encoding GI-4016are represented by SEQ ID Nos:5 and 6, respectively. GI-4016 comprisesthe following Ras mutations: Q61L-G12D-Q61R. The nucleotide andtranslated amino acid sequence for the construct encoding GI-4020 arerepresented by SEQ ID Nos:7 and 8, respectively. GI-4020 comprises thefollowing Ras mutations: Q61L-G12R-Q61H.

The invention also includes the use of homologues of any of theabove-described Ras antigens. In one aspect, the invention includes theuse of Ras antigens, having amino acid sequences that are at least 85%,86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%identical to the amino acid sequence of any one of the Ras antigensdescribed herein, including any of the Ras antigens referenced by aspecific sequence identifier herein, over the full length of the proteinor fusion protein, or with respect to a defined segment in the fusionprotein or a defined protein or domain thereof (immunogenic domain orfunctional domain (i.e., a domain with at least one biologicalactivity)) that forms part of the fusion protein. As used herein, unlessotherwise specified, reference to a percent (%) identity refers to anevaluation of homology which is performed using: (1) a BLAST 2.0 BasicBLAST homology search using blastp for amino acid searches and blastnfor nucleic acid searches with standard default parameters, wherein thequery sequence is filtered for low complexity regions by default(described in Altschul, S. F., Madden, T. L., Schaeffer, A. A., Zhang,J., Zhang, Z., Miller, W. &Lipman, D. J. (1997) “Gapped BLAST andPSI-BLAST: a new generation of protein database search programs.”Nucleic Acids Res. 25:3389-3402, incorporated herein by reference in itsentirety); (2) a BLAST 2 alignment (using the parameters describedbelow); (3) and/or PSI-BLAST with the standard default parameters(Position-Specific Iterated BLAST.

In any of the yeast-based immunotherapy compositions used in the presentinvention, the following aspects related to the yeast vehicle areincluded in the invention. According to the present invention, a yeastvehicle is any yeast cell (e.g., a whole or intact cell) or a derivativethereof (see below) that can be used in conjunction with one or moreantigens, immunogenic domains thereof or epitopes thereof in atherapeutic composition useful in the invention. The yeast vehicle cantherefore include, but is not limited to, a live intact (whole) yeastmicroorganism (i.e., a yeast cell having all its components including acell wall), a killed (dead) or inactivated intact yeast microorganism,or derivatives of intact/whole yeast including: a yeast spheroplast(i.e., a yeast cell lacking a cell wall), a yeast cytoplast (i.e., ayeast cell lacking a cell wall and nucleus), a yeast ghost (i.e., ayeast cell lacking a cell wall, nucleus and cytoplasm), a subcellularyeast membrane extract or fraction thereof (also referred to as a yeastmembrane particle and previously as a subcellular yeast particle), anyother yeast particle, or a yeast cell wall preparation. These yeastvehicles are described in detail in, e.g., U.S. Pat. No. 5,830,463, U.S.Pat. No. 7,083,787, and U.S. Pat. No. 7,736,642, the disclosures ofwhich are incorporated by reference herein.

Any yeast strain can be used to produce a yeast-based immunotherapyproduct useful in the present invention. Genera of yeast strains thatmay be used in the invention include but are not limited toSaccharomyces, Candida, Cryptococcus, Hansenula, Kluyveromyces, Pichia,Rhodotorula, Schizosaccharomyces and Yarrowia. Species of yeast strainsthat may be used in the invention include but are not limited toSaccharomyces cerevisiae, Saccharomyces carlsbergensis, Candidaalbicans, Candida kefyr, Candida tropicalis, Cryptococcus laurentii,Cryptococcus neoformans, Hansenula anomala, Hansenula polymorphs,Kluyveromyces fragilis, Kluyveromyces lactis, Kluyveromyces marxianusvar. lactis, Pichia pastoris, Rhodotorula rubra, Schizosaccharomycespombe, and Yarrowia lipolytica. It is to be appreciated that a number ofthese species include a variety of subspecies, types, subtypes, etc.that are intended to be included within the aforementioned species.

Methods for producing yeast-based immunotherapy products useful in theinvention have been previously described in, e.g. U.S. Pat. No.5,830,463, U.S. Pat. No. 7,083,787, and U.S. Pat. No. 7,736,642, thedisclosures of which are incorporated herein by reference. Typically,yeast-based immunotherapy products useful in the invention have beenkilled or inactivated. Killing or inactivating of yeast can beaccomplished by any of a variety of suitable methods known in the art.For example, heat inactivation of yeast is a standard way ofinactivating yeast, and one of skill in the art can monitor thestructural changes of the target antigen, if desired, by standardmethods known in the art. Alternatively, other methods of inactivatingthe yeast can be used, such as chemical, electrical, radioactive or UVmethods.

Yeast vehicles can be formulated into yeast-based immunotherapycompositions or products of the present invention, includingpreparations to be administered to a subject, using a number oftechniques known to those skilled in the art. For example, yeastvehicles can be dried by lyophilization. Formulations comprising yeastvehicles can also be prepared by packing yeast in a cake or a tablet,such as is done for yeast used in baking or brewing operations. Inaddition, yeast vehicles can be mixed with a pharmaceutically acceptableexcipient, such as an isotonic buffer that is tolerated by a host orhost cell. Examples of such excipients include water, saline, Ringer'ssolution, dextrose solution, Hank's solution, and other aqueousphysiologically balanced salt solutions. Nonaqueous vehicles, such asfixed oils, sesame oil, ethyl oleate, or triglycerides may also be used.Other useful formulations include suspensions containingviscosity-enhancing agents, such as sodium carboxymethylcellulose,sorbitol, glycerol or dextran. Excipients can also contain minor amountsof additives, such as substances that enhance isotonicity and chemicalstability. Standard formulations can either be liquid injectables orsolids which can be taken up in a suitable liquid as a suspension orsolution for injection. Thus, in a non-liquid formulation, the excipientcan comprise, for example, dextrose, human serum albumin, and/orpreservatives to which sterile water or saline can be added prior toadministration. The composition should be formulated to be suitable foradministration to a human subject (e.g., the manufacturing conditionsshould be suitable for use in humans, and any excipients or formulationsused to finish the composition and/or prepare the dose of theimmunotherapeutic for administration should be suitable for use inhumans). In one aspect of the invention, yeast-based immunotherapeuticcompositions are formulated for administration by injection of thepatient or subject, such as by a parenteral route (e.g., bysubcutaneous, intraperitoneal, intramuscular or intradermal injection,or another suitable parenteral route).

The therapeutic methods include the delivery (administration,immunization) of a yeast-based immunotherapeutic composition to asubject or individual. The administration process can be performed exvivo or in vivo, but is typically performed in vivo. Administration of ayeast-based immunotherapy composition can be systemic, mucosal and/orproximal to the location of the target site (e.g., near a site of atumor). Suitable routes of administration will be apparent to those ofskill in the art, depending on the type of cancer to be prevented ortreated and/or the target cell population or tissue. Various acceptablemethods of administration include, but are not limited to, intravenousadministration, intraperitoneal administration, intramuscularadministration, intranodal administration, intracoronary administration,intraarterial administration (e.g., into a carotid artery), subcutaneousadministration, transdermal delivery, intratracheal administration,intraarticular administration, intraventricular administration,inhalation (e.g., aerosol), intracranial, intraspinal, intraocular,aural, intranasal, oral, pulmonary administration, impregnation of acatheter, and direct injection into a tissue. In one aspect, routes ofadministration include: intravenous, intraperitoneal, subcutaneous,intradermal, intranodal, intramuscular, transdermal, inhaled,intranasal, oral, intraocular, intraarticular, intracranial, andintraspinal. Parenteral delivery can include intradermal, intramuscular,intraperitoneal, intrapleural, intrapulmonary, intravenous,subcutaneous, atrial catheter and venal catheter routes. Aural deliverycan include ear drops, intranasal delivery can include nose drops orintranasal injection, and intraocular delivery can include eye drops.Aerosol (inhalation) delivery can also be performed using methodsstandard in the art (see, for example, Stribling et al., Proc. Natl.Acad. Sci. USA 189:11277-11281, 1992). In one aspect, a yeast-basedimmunotherapeutic composition of the invention is administeredsubcutaneously. In one aspect, the yeast-based immunotherapeuticcomposition is administered directly into a tumor milieu.

In general, a suitable single dose of a yeast-based immunotherapeuticcomposition is a dose that is capable of effectively providing a yeastvehicle and the cancer antigen to a given cell type, tissue, or regionof the patient body in an amount effective to elicit an antigen-specificimmune response against one or more cancer antigens or epitopes, whenadministered one or more times over a suitable time period. For example,in one embodiment, a single dose of a yeast-based immunotherapeuticuseful in the present invention is from about 1×10⁵ to about 5×10⁷ yeastcell equivalents per kilogram body weight of the organism beingadministered the composition. In one aspect, a single dose of ayeast-based immunotherapeutic useful in the present invention is fromabout 0.1 Y.U. (1×10⁶ cells) to about 100 Y.U. (1×10⁹ cells) per dose(i.e., per organism), including any interim dose, in increments of0.1×10⁶ cells (i.e., 1.1×10⁶, 1.2×10⁶, 1.3×10⁶ . . . ). As used herein,the term “Y.U.” is a “Yeast Unit” or “yeast cell equivalent, where oneY.U.=10 million yeast cells. In one embodiment, doses include dosesbetween 1 Y.U and 40 Y.U., doses between 1 Y.U. and 50 Y.U., dosesbetween 1 Y.U. and 60 Y.U., doses between 1 Y.U. and 70 Y.U., or dosesbetween 1 Y.U. and 80 Y.U., and in one aspect, between 10 Y.U. and 40Y.U., 50 Y.U., 60 Y.U., 70 Y.U., or 80 Y.U. In one embodiment, the dosesare administered at different sites on the individual but during thesame dosing period. For example, a 40 Y.U. dose may be administered viaby injecting 10 Y.U. doses to four different sites on the individualduring one dosing period, or a 20 Y.U. dose may be administered byinjecting 5 Y.U. doses to four different sites on the individual, or byinjecting 10 Y.U. doses to two different sites on the individual, duringthe same dosing period. The invention includes administration of anamount of the yeast-based immunotherapy composition (e.g., 1, 2, 3, 4,5, 6, 7, 8, 9 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 Y.U. or more)at 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more different sites on anindividual to form a single dose.

“Boosters” or “boosts” of yeast-based immunotherapeutic composition areadministered, for example, when the immune response against the antigenhas waned or as needed to provide an immune response or induce a memoryresponse against a particular antigen or antigen(s). Boosters can beadministered from about 1, 2, 3, 4, 5, 6, 7, or 8 weeks apart, tomonthly, to bimonthly, to quarterly, to annually, to several years afterthe original administration. In one embodiment, an administrationschedule is one in which a single dose is administered at least 1, 2, 3,4, 5, 6, 7, 8, 9, 10, or more times over a time period of from weeks, tomonths, to years. In one embodiment, the doses are administered weeklyfor 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or more doses, followed by monthlydoses as needed to achieve the desired therapeutic result. Additionaldoses can be administered even if the patient's tumor recurs, or afterthe patient is deemed to be in remission.

In one aspect, the individual is additionally treated with at least oneother therapeutic compound or therapeutic protocol useful for thetreatment of cancer (anti-cancer therapy). Additional agents,compositions or protocols (e.g., therapeutic protocols) that are usefulfor the treatment of cancer include, but are not limited to,chemotherapy, surgical resection of a tumor, radiation therapy,allogeneic or autologous stem cell transplantation, cytokine therapy,adoptive T cell transfer, and/or administration of a secondimmunotherapeutic composition (e.g., additional yeast-basedimmunotherapy, recombinant virus-based immunotherapy (viral vectors),cytokine therapy, immunostimulant therapy (including chemotherapy withimmunostimulating properties), DNA vaccines, and other immunotherapycompositions and/or targeted cancer therapies (e.g., small moleculedrugs, biologics, or monoclonal antibody therapies that specificallytarget molecules involved in tumor growth and progression, including,but not limited to, selective estrogen receptor modulators (SERMs),aromatase inhibitors, tyrosine kinase inhibitors, serine/threoninekinase inhibitors, histone deacetylase (HDAC) inhibitors, retinoidreceptor activators, apoptosis stimulators, angiogenesis inhibitors,poly (ADP-ribose) polymerase (PARP) inhibitors, orimmunostimulators).Any of these additional therapeutic agents and/ortherapeutic protocols may be administered before, concurrently with,alternating with, or after the immunotherapy compositions of theinvention, or at different time points. For example, when given to anindividual in conjunction with chemotherapy or a targeted cancertherapy, it may be desirable to administer the yeast-based immunotherapycompositions during the “holiday” between doses of chemotherapy ortargeted cancer therapy, in order to maximize the efficacy of theimmunotherapy compositions. Surgical resection of a tumor may frequentlyprecede administration of a yeast-based immunotherapy composition, butadditional or primary surgery may occur during or after administrationof a yeast-based immunotherapy composition.

For example, in any of the embodiments regarding therapeutic methods ofthe invention described herein, in one aspect, when the individual hascancer, the individual is being treated or has been treated with anothertherapy for cancer. Such therapy can include any of the therapeuticprotocols or use of any therapeutic compound or agent describedpreviously herein, including, but not limited to, chemotherapy,radiation therapy, targeted cancer therapy, surgical resection of atumor, stem cell transfer, cytokine therapy, adoptive T cell transfer,and/or administration of a second immunotherapeutic composition. In thecase of administration of a second immunotherapeutic composition, suchcompositions may include, but are not limited to, additional yeast-basedimmunotherapy, recombinant virus-based immunotherapy (viral vectors),immunostimulant therapy (including chemotherapy with immunostimulatingproperties), DNA vaccines, and other immunotherapy compositions).

As used herein, to “treat” a cancer, or any permutation thereof (e.g.,“treated for cancer”, etc.) generally refers to administering ayeast-based immunotherapy composition of the invention once the cancerhas occurred (e.g., once the cancer has been diagnosed or detected in anindividual), with at least one therapeutic goal of the treatment (ascompared to in the absence of this treatment) including: reduction intumor burden, inhibition of tumor growth, increase in recurrence freesurvival of the individual, increase in overall survival of theindividual, delaying, inhibiting, arresting or preventing the onset ordevelopment of metastatic cancer (such as by delaying, inhibiting,arresting or preventing the onset of development of tumor migrationand/or tumor invasion of tissues outside of primary cancer and/or otherprocesses associated with metastatic progression of cancer), delaying orarresting cancer progression, improvement of immune responses againstthe tumor, improvement of long term memory immune responses against thetumor antigens, and/or improved general health of the individual. To“prevent” or “protect” from a cancer, or any permutation thereof (e.g.,“prevention of cancer”, etc.), generally refers to administering acomposition of the invention before a cancer has occurred, or before aspecific stage of cancer or tumor antigen expression in a cancer hasoccurred, with at least one goal of the treatment (as compared to in theabsence of this treatment) including: preventing or delaying the onsetor development of a cancer, or, should the cancer occur after thetreatment, at least reducing the severity of the cancer (e.g., reducingthe level of tumor growth, arresting cancer progression, improving theimmune response against the cancer, inhibiting metastatic processes) orimproving outcomes in the individual (e.g., improving recurrence-freesurvival and/or overall survival).

In the therapeutic methods of the present invention, yeast-basedimmunotherapy compositions and other anti-cancer therapies can beadministered to any animal, including any vertebrate, and particularlyto any member of the Vertebrate class, Mammalia, including, withoutlimitation, primates, rodents, livestock and domestic pets. An“individual” is a vertebrate, such as a mammal, including withoutlimitation a human. The term “individual” can be used interchangeablywith the term “animal”, “subject” or “patient”.

According to the present invention, the general use herein of the term“antigen” refers: to any portion of a protein (peptide, partial protein,full-length protein), wherein the protein is naturally occurring orsynthetically derived, to a cellular composition (whole cell, celllysate or disrupted cells), to an organism (whole organism, lysate ordisrupted cells) or to a carbohydrate, or other molecule, or a portionthereof. An antigen may elicit an antigen-specific immune response(e.g., a humoral and/or a cell-mediated immune response) against thesame or similar antigens that are encountered by an element of theimmune system (e.g., T cells, antibodies).

An antigen can be as small as a single epitope, a single immunogenicdomain or larger, and can include multiple epitopes or immunogenicdomains. As such, the size of an antigen can be as small as about 8-12amino acids (i.e., a peptide) and as large as: a full length protein, amultimer, a fusion protein, a chimeric protein, a whole cell, a wholemicroorganism, or any portions thereof (e.g., lysates of whole cells orextracts of microorganisms). In addition, antigens can includecarbohydrates, which can be loaded into a yeast vehicle or into acomposition of the invention. It will be appreciated that in someembodiments (e.g., when the antigen is expressed by the yeast vehiclefrom a recombinant nucleic acid molecule), the antigen is a protein,fusion protein, chimeric protein, or fragment thereof, rather than anentire cell or microorganism.

When the antigen is to be expressed in yeast, an antigen is of a minimumsize capable of being expressed recombinantly in yeast, and is typicallyat least or greater than 25 amino acids in length, or at least orgreater than 26, at least or greater than 27, at least or greater than28, at least or greater than 29, at least or greater than 30, at leastor greater than 31, at least or greater than 32, at least or greaterthan 33, at least or greater than 34, at least or greater than 35, atleast or greater than 36, at least or greater than 37, at least orgreater than 38, at least or greater than 39, at least or greater than40, at least or greater than 41, at least or greater than 42, at leastor greater than 43, at least or greater than 44, at least or greaterthan 45, at least or greater than 46, at least or greater than 47, atleast or greater than 48, at least or greater than 49, or at least orgreater than 50 amino acids in length, or is at least 25-50 amino acidsin length, at least 30-50 amino acids in length, or at least 35-50 aminoacids in length, or at least 40-50 amino acids in length, or at least45-50 amino acids in length. Smaller proteins may be expressed, andconsiderably larger proteins (e.g., hundreds of amino acids in length oreven a few thousand amino acids in length) may be expressed. In oneaspect, a full-length protein, or a structural or functional domainthereof, or an immunogenic domain thereof, that is lacking one or moreamino acids from the N- and/or the C-terminus may be expressed (e.g.,lacking between about 1 and about 20 amino acids from the N- and/or theC-terminus). Fusion proteins and chimeric proteins are also antigensthat may be expressed in the invention. A “target antigen” is an antigenthat is specifically targeted by an immunotherapeutic composition of theinvention (i.e., an antigen against which elicitation of an immuneresponse is desired). For example, a “Ras antigen” is an antigenderived, designed, or produced from one or more Ras proteins such thattargeting the antigen also targets the corresponding Ras proteinexpressed by a tumor. A “mutated Ras antigen” refers specifically to aRas antigen that contains one or more amino acid mutations. For use inthe present invention, such mutations correspond to mutations found inthe Ras protein in tumors and are associated with the development of thetumor and/or the progression of the tumor.

When referring to stimulation of an immune response, the term“immunogen” is a subset of the term “antigen”, and therefore, in someinstances, can be used interchangeably with the term “antigen”. Animmunogen, as used herein, describes an antigen which elicits a humoraland/or cell-mediated immune response (i.e., is immunogenic), such thatadministration of the immunogen to an individual mounts anantigen-specific immune response against the same or similar antigensthat are encountered by the immune system of the individual. In oneembodiment, an immunogen contained in a yeast-based immunotherapycomposition elicits a cell-mediated immune response, including a CD4+ Tcell response (e.g., TH1, TH2 and/or TH17) and/or a CD8+ T cell response(e.g., a CTL response).

An “immunogenic domain” of a given antigen can be any portion, fragmentor epitope of an antigen (e.g., a peptide fragment or subunit or anantibody epitope or other conformational epitope) that contains at leastone epitope that acts as an immunogen when administered to an animal.Therefore, an immunogenic domain is larger than a single amino acid andis at least of a size sufficient to contain at least one epitope thatcan act as an immunogen. For example, a single protein can containmultiple different immunogenic domains. Immunogenic domains need not belinear sequences within a protein, such as in the case of a humoralimmune response, where conformational domains are contemplated.

An epitope is defined herein as a single immunogenic site within a givenantigen that is sufficient to elicit an immune response when provided tothe immune system in the context of appropriate costimulatory signalsand/or activated cells of the immune system. In other words, an epitopeis the part of an antigen that is actually recognized by components ofthe immune system, and may also be referred to as an antigenicdeterminant. Those of skill in the art will recognize that T cellepitopes are different in size and composition from B cell or antibodyepitopes, and that epitopes presented through the Class I MHC pathwaydiffer in size and structural attributes from epitopes presented throughthe Class II MHC pathway. For example, T cell epitopes presented byClass I MHC molecules are typically between 8 and 11 amino acids inlength, whereas epitopes presented by Class II MHC molecules are lessrestricted in length and may be from 8 amino acids up to 25 amino acidsor longer. In addition, T cell epitopes have predicted structuralcharacteristics depending on the specific MHC molecules bound by theepitope. Epitopes can be linear sequence epitopes or conformationalepitopes (conserved binding regions). Most antibodies recognizeconformational epitopes.

While various embodiments of the present invention have been describedin detail, it is apparent that modification and adaptations from thoseembodiments will occur to those skilled in the art. It will be expresslyunderstood, however, that such modifications and adaptations are withinthe scope of the present invention, as set forth in the appended claims.

We claim:
 1. A method of predicting whether a cancer patient is likelyto benefit from administration of a yeast-based immune responsegenerating therapy for cancer, either alone or in addition to anotheranti-cancer therapy, comprising the steps of: (a) obtaining ablood-derived sample of the patient; (b) conducting mass-spectrometry onthe sample and obtaining a mass spectrum from the sample; (c) in aprogrammed computer, performing one or more predefined pre-processingsteps on the mass spectrum, obtaining integrated intensity values ofselected features in the mass spectrum over predefined m/z ranges afterthe pre-processing steps are performed, and comparing the integratedintensity values with a training set comprising class-labeled spectrafrom other cancer patients and classifying the mass spectrum with aclass label; wherein the class label predicts whether the patient islikely to benefit from the yeast-based immune response generatingtherapy, either alone or in addition to other anti-cancer therapies. 2.The method of claim 1, wherein the yeast-based immunotherapy comprises ayeast-based immunotherapy for mutated Ras-positive cancer.
 3. The methodof claim 3, wherein the yeast-based immunotherapy for mutatedRas-positive cancer comprises GI-4000 or the equivalent.
 4. The methodof claim 3, wherein the yeast-based immunotherapy for mutatedRas-positive cancer therapy is administered to the patient inconjunction with gemcitabine or the equivalent.
 5. The method of claim1, wherein the cancer patient comprises a pancreatic cancer patient. 6.The method of claim 2, wherein the cancer patient comprises a pancreaticcancer patient.
 7. The method of claim 1, wherein the training setcomprises class-labeled spectra from other cancer patients who obtainedbenefit and did not obtain benefit from administration of theyeast-based immune response generating therapy or immunotherapy eitheralone or in combination with another anti-cancer therapy.
 8. The methodof claim 1, wherein the selected features include one or more featureslisted in Tables 1, 2, 3, 4, 5, or
 6. 9. A method of predicting whethera cancer patient is not likely to benefit from administration of ayeast-based immune response generating therapy, either alone or incombination with another anti-cancer therapy, comprising the steps of:(a) obtaining a blood-derived sample of the patient; (b) conductingmass-spectrometry on the sample and obtaining a mass spectrum from thesample; (c) in a programmed computer, performing one or more predefinedpre-processing steps on the mass spectrum, obtaining integratedintensity values of selected features in the mass spectrum overpredefined m/z ranges after the pre-processing steps are performed, andcomparing the integrated intensity values with a training set comprisingclass-labeled spectra from other cancer patients and classifying themass spectrum with a class label; wherein the class label predictswhether the patient is not likely to benefit from the yeast-based immuneresponse generating therapy either alone or in addition to anotheranti-cancer therapy.
 10. The method of claim 9, wherein the yeast-basedimmunotherapy for cancer comprises a yeast-based immunotherapy formutated Ras-positive cancer.
 11. The method of claim 10, wherein theyeast-based immunotherapy for mutated Ras-positive cancer comprisesGI-4000 or the equivalent.
 12. The method of claim 10, wherein thepatient is predicted to not likely benefit from administration of theyeast-based immunotherapy for mutated Ras-positive cancer therapy inconjunction with gemcitabine or the equivalent.
 13. The method of claim9, wherein the cancer patient comprises a pancreatic cancer patient. 14.The method of claim 9, wherein the training set comprises class-labeledspectra from other cancer patients that obtained benefit and did notobtain benefit from administration of the immunotherapy either alone orin combination with another anti-cancer therapy.
 15. The method of claim9, wherein the selected features comprise one or more features listed inTables 1, 2, 3, 4, 5 or
 6. 16. A system for predicting whether a cancerpatient is likely to benefit from administration of a yeast-based immuneresponse generating therapy either alone or in combination with anotheranti-cancer therapy, comprising, in combination: a mass spectrometergenerating a mass spectrum from a blood-derived sample from the cancerpatient; a machine readable memory storing a training set ofclass-labeled spectra from other cancer patients, the training setincluding class-labeled spectra from plurality of patients that did notbenefit from the yeast-based immune response generating either alone orin combination with the another anti-cancer therapy and class-labeledspectra from plurality of patients that did benefit from the immuneresponse generating therapy either alone or in combination with theanother anti-cancer agent; and a computer system configured to operateon the mass spectrum and classify the mass spectrum using the trainingset, producing a class label for the mass spectrum, wherein the classlabel predicts whether the patient is likely to benefit fromadministration of the yeast-based immune response generating therapyeither alone or in combination with another anti-cancer agent.
 17. Thesystem of claim 16, wherein the class label predicts whether the patientis likely to benefit from administration of a yeast-based immunotherapyfor mutated Ras-positive cancer.
 18. The system of claim 16, wherein theyeast-based immunotherapy for mutated Ras-positive cancers comprisesGI-4000 or the equivalent.
 19. The system of claim 16, wherein the classlabel predicts whether patient is likely to benefit from administrationof GI-4000 and gemcitabine to the patient.
 20. The system of claim 16,wherein the cancer patient comprises a pancreatic cancer patient. 21.The system of claim 16, wherein the computer is configured to obtainintegrated intensity values of features in the spectrum in m/z rangeswhich include one or more of the features listed in Tables 1, 2, 3, 4, 5or
 6. 22. A system for predicting whether a cancer patient is not likelyto obtain benefit from administration of a yeast-based immune responsegenerating therapy either alone or in combination with anotheranti-cancer therapy, comprising, in combination: a mass spectrometergenerating a mass spectrum from a blood-derived sample from the cancerpatient; a machine readable memory storing a training set ofclass-labeled spectra from other cancer patients, the training setincluding class-labeled spectra from plurality of patients that did notbenefit from the immune response generating therapy either alone or incombination with the another anti-cancer therapy and class-labeledspectra from plurality of patients that did benefit from the immuneresponse generating therapy either alone or in combination with theanother anti-cancer agent; and a computer system configured to operateon the mass spectrum and classify the mass spectrum using the trainingset, producing a class label for the mass spectrum, wherein the classlabel predicts whether the patient is not likely to obtain benefit froma yeast-based immunotherapy either alone or in combination with anotheranti-cancer agent.
 23. The system of claim 22, wherein the yeast-basedimmunotherapy for cancer comprises a yeast-based immunotherapy formutated Ras-positive cancer.
 24. The system of claim 23, wherein theyeast-based immunotherapy for mutated Ras-positive cancer comprisesGI-4000 or the equivalent.
 25. The system of claim 24, wherein the classlabel predicts whether the patient is not likely to benefit fromadministration of GI-4000 and gemcitabine.
 26. The system of claim 24,wherein the cancer patient comprises a pancreatic cancer patient. 27.The system of claim 22, wherein the computer is configured to obtainintegrated intensity values of features in the spectrum in m/z rangeswhich include one or more of the features listed in Table 1, 2, 3, 4 5or
 6. 28. The method of claim 1, wherein the step of conducting massspectrometry on the sample comprises conducting MALDI TOF massspectrometry.
 29. The method of claim 28, wherein the MALDI-TOF massspectrometry comprises conducting DeepMALDI mass spectrometry in whichthe blood-derived sample is subject to more than 20,000 laser shots. 30.The method of claim 1, wherein the predefined m/z regions are obtainedfrom investigation of a plurality of mass spectra of blood-derivedsamples of cancer patients, each of the mass spectra obtained byDeepMALDI mass spectrometry in which the blood-derived samples aresubject to more than 20,000 laser shots.
 31. The system of claim 16,wherein the mass spectrometer comprises a MALDI-TOF mass spectrometerconfigured to obtain spectra by subjecting the blood-derived sample tomore than 20,000 laser shots.
 32. A non-transitory computer readablemedium storing data in the form of a training set for a classifiercomprising class-labeled mass spectra from a plurality of cancerpatients, including class-labeled mass spectra from plurality of cancerpatients that did not benefit from a yeast-based immune responsegenerating therapy either alone or in combination with the anotheranti-cancer therapy and class-labeled mass spectra from a plurality ofcancer patients that did benefit from the yeast-based immune responsegenerating therapy either alone or in combination with the anotheranti-cancer therapy, the class label indicating the status of benefit ornon-benefit.
 33. The non-transitory computer readable medium of claim32, wherein the medium stores data in the form of a training set for aclassifier comprising class-labeled mass spectra from a plurality ofcancer patients, including class-labeled mass spectra from plurality ofcancer patients that did not benefit from yeast-based immunotherapy formutated Ras-positive cancer either alone or in conjunction with theanother anti-cancer therapy and class-labeled mass spectra fromplurality of cancer patients that did benefit from the yeast-basedimmunotherapy for mutated Ras-positive cancer either alone or inconjunction with the another anti-cancer therapy, the class labelindicating the status of benefit or non-benefit.
 34. The medium of claim33, wherein the class labels indicate whether the patients in thetraining set benefitted, or did not benefit, from administration of thecombination of GI-4000 and gemcitabine.
 35. The medium of claim 32,wherein the cancer patients in the training set comprise pancreaticcancer patients.
 36. The medium of claim 32, wherein the medium storescode in the form of a classifier for comparing features in the trainingset including one or more of features listed in Tables 1, 2, 3 or 4 witha test mass spectrum to thereby produce a class label for the test massspectrum.
 37. A system for classifying a mass spectrum comprising: anon-transitory computer-readable as recited in claim 32 and a generalpurpose computer configured as a classifier to classify a spectrum of acancer patient using the training set of claim
 32. 38. The system ofclaim 37, wherein the computer implements a K nearest neighborclassifier.
 39. A method of creating a classifier for predicting, inadvance of treatment, whether a cancer patient is likely to benefit froma yeast-based immune response generating therapy, either alone or incombination with another anti-cancer therapy, comprising the steps of:a) performing mass spectrometry on a multitude of patient samplesobtained after treatment by the yeast-based immune response generatingtherapy either alone or in combination with another anti-cancer therapyand generating a corresponding set of mass spectra; b) creating atraining set for a classifier by separating the patients from which thesamples in step a) were obtained into two groups corresponding towhether or not such patients obtained benefit from the treatment andassigning a class label to the respective spectra (e.g., “good”/“poor”,“Quick”/“Slow”); c) analyzing the spectra from the two groups toidentify distinguishing features in the spectra characteristic of thepatients in the two groups; and d) testing the ability of suchdistinguishing features to predict patient benefit from the treatment byapplying a classification algorithm using the class labeled spectra anddistinguishing features identified in step c) to a test set of spectrafrom other patients that were administered the treatment and evaluatingthe performance of the classification algorithm.
 40. The method of claim39, comprising repeating the steps b), c) and d) using differentdistinguishing features in the spectra in the two groups of patients.41. The method of claim 39, wherein the classification algorithmcomprises a K-nearest neighbor algorithm.
 42. The method of claim 39,wherein the mass spectrometry of step a) comprises DeepMALDI massspectrometry in a MALDI-TOF mass spectrometer in which the sample issubject to more than 20,000 laser shots.
 43. The method of claim 39,wherein the distinguishing features comprise one or more features listedin Tables 1, 2, 3, 4, 5 or
 6. 44. The method of claim 39, furthercomprising performing a partial ion current normalization of the spectrabased on features in the spectra in the training set.
 45. The method ofclaim 39, wherein the class labels are determined by investigation ofclinical data for the patients in the training set.
 46. A method fortreating a cancer, comprising administering yeast-based immunotherapyfor cancer to a cancer patient, wherein the patient has been selected bya mass spectrometry and classification test, the test comprising thesteps of: (a) conducting mass-spectrometry on a blood-derived samplefrom the patient and obtaining a mass spectrum from the sample; (b) in aprogrammed computer, performing one or more predefined pre-processingsteps on the mass spectrum, obtaining integrated intensity values ofselected features in the mass spectrum over predefined m/z ranges afterthe pre-processing steps are performed, and comparing the integratedintensity values with a training set comprising class-labeled spectrafrom other cancer patients treated with the yeast-based immunotherapyand classifying the mass spectrum with a class label; and wherein theclass label for the mass spectrum indicates that the patient is likelyto benefit from the yeast-based immunotherapy for cancer.
 47. The methodof claim 46, wherein the patient is additionally treated with one ormore additional anti-cancer therapies, either prior to, concurrentlywith, or after, treatment with the yeast-based immunotherapy for cancer.48. The method of claim 47, wherein the additional anti-cancer therapiesare selected from: surgery, chemotherapy, radiation therapy, targetedcancer therapy, palliative care, or any combination thereof.
 49. Themethod of claim 46, wherein the yeast-based immunotherapy is a whole,heat-inactivated recombinant yeast that has expressed one or more cancerantigens from the patient's cancer.
 50. The method of claim 49, whereinthe yeast is from Saccharomyces cerevisiae.
 51. The method of claim 46,wherein the patient has mutated Ras-positive cancer and wherein thepatient is administered a yeast-based immunotherapy for mutatedRas-positive cancer
 52. The method of claim 51, wherein the patient isadditionally treated with one or more additional anti-cancer therapies,either prior to, concurrently with, or after, treatment with theyeast-based immunotherapy for mutated Ras-positive cancer.
 53. Themethod of claim 52, wherein the additional anti-cancer therapies areselected from: surgery, chemotherapy, radiation therapy, targeted cancertherapy, palliative care, or any combination thereof.
 54. The method ofclaim 51, wherein the mutated Ras-positive cancer is selected from:pancreas cancer, non-small cell lung cancer (NSCLC), colorectal cancer(CRC), endometrial cancers, ovarian cancers, melanoma or multiplemyeloma.
 55. The method of claim 51, wherein the mutated Ras-positivecancer is pancreas cancer.
 56. The method of claim 51, wherein theyeast-based immunotherapy for mutated Ras-positive cancer is a whole,heat-inactivated recombinant yeast that has expressed a mutated Rasantigen.
 57. The method of claim 55, wherein the yeast-basedimmunotherapy for mutated Ras-positive cancer is a product in theGI-4000 series of yeast-based immunotherapy products, or the equivalent.58. The method of claim 57, wherein the product is selected fromGI-4014, GI-4015, GI-4016 or GI-4020.
 59. The method of claim 51,wherein the yeast-based immunotherapy is administered in conjunctionwith gemcitabine or the equivalent.
 60. The method of claim 51, whereinthe cancer patient's tumor has been surgically resected prior totreatment with the yeast-based immunotherapy.
 61. The method of claim49, wherein the training set comprises class-labeled spectra from othercancer patients who obtained benefit and did not obtain benefit fromadministration of the yeast-based immunotherapy either alone or incombination with another anti-cancer therapy.
 62. The method of claim49, wherein the predefined features include one or more features listedin Tables 1, 2, 3, 4, 5 or
 6. 63. The method of claim 49, wherein themass spectrometry conducted on the sample comprises conducting MALDI-TOFmass spectrometry.
 64. The method of claim 63, wherein the MALDI-TOFmass spectrometry comprises conducting DeepMALDI mass spectrometry inwhich the blood-derived sample is subject to more than 20,000 lasershots.
 65. The method of claim 49, wherein the predefined m/z regionsare obtained from investigation of a plurality of mass spectra of cancerpatients obtained by DeepMALDI mass spectrometry in which blood-derivedsamples of such cancer patients are subject to more than 20,000 lasershots.
 66. A method for treating a cancer with yeast-basedimmunotherapy, comprising: a) conducting mass-spectrometry on ablood-derived sample from a cancer patient and obtaining a mass spectrumfrom the sample; b) in a programmed computer, performing one or morepredefined pre-processing steps on the mass spectrum, obtainingintegrated intensity values of selected features in the mass spectrumover predefined m/z ranges after the pre-processing steps are performed,and comparing the integrated intensity values with a training setcomprising class-labeled spectra from other cancer patients treated withthe yeast-based immunotherapy and classifying the mass spectrum with aclass label; and c) administering the yeast-based immunotherapy forcancer to the cancer patient when the class label for the mass spectrumindicates that the patient is likely to benefit from the yeast-basedimmunotherapy for cancer.
 67. The method of claim 66, wherein thepatient is additionally treated with one or more additional anti-cancertherapies, either prior to, concurrently with, or after, treatment withthe yeast-based immunotherapy for cancer.
 68. The method of claim 66,wherein the yeast-based immunotherapy is a whole, heat-inactivatedrecombinant yeast from Saccharomyces cerevisiae that has expressed oneor more cancer antigens from the patient's cancer.
 69. The method ofclaim 66, wherein the patient has mutated Ras-positive cancer andwherein the patient is administered a yeast-based immunotherapy formutated Ras-positive cancer.
 70. The method of claim 69, wherein theproduct is selected from GI-4014, GI-4015, GI-4016 or GI-4020.
 71. Themethod of claim 66, wherein the training set comprises class-labeledspectra from other cancer patients who obtained benefit and did notobtain benefit from administration of the yeast based immunotherapyeither alone or in combination with another anti-cancer therapy.
 72. Themethod of claim 66, wherein the predefined features include one or morefeatures listed in Tables 1, 2, 3, 4, 5 or
 6. 73. The method of claim66, wherein the mass spectrometry conducted on the sample comprisesconducting MALDI-TOF mass spectrometry.
 74. The method of claim 73,wherein the MALDI-TOF mass spectrometry comprises conducting multi-shotmass spectrometry in which the blood-derived sample is subject to morethan 20,000 laser shots.
 75. The method of claim 66, wherein thepredefined m/z regions are obtained from investigation of a plurality ofmass spectra of cancer patients obtained by multi-shot mass spectrometryin which blood-derived samples of such cancer patients are subject tomore than 20,000 laser shots.